{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Variogram Calculation Demonstration\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive workflow for calculating directional experimental variograms in 2D. \n",
    "\n",
    "* setting the variogram calculation parameters for identifying spatial data pairs \n",
    "\n",
    "This approach is essential for quantifying spatial continuity with sparsely sampled, irregular spatial data.\n",
    "\n",
    "I have more comprehensive workflows for variogram calculation:\n",
    "\n",
    "* [Experimental Variogram Calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n",
    "\n",
    "* [Determination of Major and Minor Spatial Continuity Directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "The variogram calculation parameters include:\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n",
    "\n",
    "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n",
    "\n",
    "* **bandwidth** is the maximum offset allowable from the lag vector \n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"d:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101319</td>\n",
       "      <td>1.996868</td>\n",
       "      <td>5590.417154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.147676</td>\n",
       "      <td>10.711789</td>\n",
       "      <td>3470.845666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.145912</td>\n",
       "      <td>17.818143</td>\n",
       "      <td>3586.988513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.186167</td>\n",
       "      <td>217.109365</td>\n",
       "      <td>3732.114787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.146088</td>\n",
       "      <td>16.717367</td>\n",
       "      <td>2534.551236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      X      Y  Facies  Porosity        Perm           AI\n",
       "0           0  100.0  900.0     0.0  0.101319    1.996868  5590.417154\n",
       "1           1  100.0  800.0     1.0  0.147676   10.711789  3470.845666\n",
       "2           2  100.0  700.0     1.0  0.145912   17.818143  3586.988513\n",
       "3           3  100.0  600.0     1.0  0.186167  217.109365  3732.114787\n",
       "4           4  100.0  500.0     1.0  0.146088   16.717367  2534.551236"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv(\"sample_data_MV_biased.csv\")           # read a .csv file in as a DataFrame\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\")\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>368.0</td>\n",
       "      <td>293.260870</td>\n",
       "      <td>169.058258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.500000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>439.500000</td>\n",
       "      <td>586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>368.0</td>\n",
       "      <td>499.565217</td>\n",
       "      <td>289.770794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>762.500000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>368.0</td>\n",
       "      <td>520.644022</td>\n",
       "      <td>277.412187</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>769.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>368.0</td>\n",
       "      <td>0.597826</td>\n",
       "      <td>0.491004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>368.0</td>\n",
       "      <td>0.127026</td>\n",
       "      <td>0.030642</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.103412</td>\n",
       "      <td>0.125842</td>\n",
       "      <td>0.148623</td>\n",
       "      <td>0.210258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>368.0</td>\n",
       "      <td>85.617362</td>\n",
       "      <td>228.362654</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>2.297348</td>\n",
       "      <td>10.377292</td>\n",
       "      <td>50.581288</td>\n",
       "      <td>1991.097723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>368.0</td>\n",
       "      <td>4791.736646</td>\n",
       "      <td>974.560569</td>\n",
       "      <td>1981.177309</td>\n",
       "      <td>4110.728374</td>\n",
       "      <td>4713.325533</td>\n",
       "      <td>5464.043562</td>\n",
       "      <td>7561.250336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean         std          min          25%  \\\n",
       "Unnamed: 0  368.0   293.260870  169.058258     0.000000   150.500000   \n",
       "X           368.0   499.565217  289.770794     0.000000   240.000000   \n",
       "Y           368.0   520.644022  277.412187     9.000000   269.000000   \n",
       "Facies      368.0     0.597826    0.491004     0.000000     0.000000   \n",
       "Porosity    368.0     0.127026    0.030642     0.041122     0.103412   \n",
       "Perm        368.0    85.617362  228.362654     0.094627     2.297348   \n",
       "AI          368.0  4791.736646  974.560569  1981.177309  4110.728374   \n",
       "\n",
       "                    50%          75%          max  \n",
       "Unnamed: 0   296.000000   439.500000   586.000000  \n",
       "X            500.000000   762.500000   990.000000  \n",
       "Y            539.000000   769.000000   999.000000  \n",
       "Facies         1.000000     1.000000     1.000000  \n",
       "Porosity       0.125842     0.148623     0.210258  \n",
       "Perm          10.377292    50.581288  1991.097723  \n",
       "AI          4713.325533  5464.043562  7561.250336  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                               # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the porosity and permeaiblity data to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101319</td>\n",
       "      <td>1.996868</td>\n",
       "      <td>5590.417154</td>\n",
       "      <td>-0.749088</td>\n",
       "      <td>-0.767247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.147676</td>\n",
       "      <td>10.711789</td>\n",
       "      <td>3470.845666</td>\n",
       "      <td>0.653263</td>\n",
       "      <td>0.017030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.145912</td>\n",
       "      <td>17.818143</td>\n",
       "      <td>3586.988513</td>\n",
       "      <td>0.611663</td>\n",
       "      <td>0.336607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.186167</td>\n",
       "      <td>217.109365</td>\n",
       "      <td>3732.114787</td>\n",
       "      <td>1.993601</td>\n",
       "      <td>1.211919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.146088</td>\n",
       "      <td>16.717367</td>\n",
       "      <td>2534.551236</td>\n",
       "      <td>0.628172</td>\n",
       "      <td>0.279461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      X      Y  Facies  Porosity        Perm           AI  \\\n",
       "0           0  100.0  900.0     0.0  0.101319    1.996868  5590.417154   \n",
       "1           1  100.0  800.0     1.0  0.147676   10.711789  3470.845666   \n",
       "2           2  100.0  700.0     1.0  0.145912   17.818143  3586.988513   \n",
       "3           3  100.0  600.0     1.0  0.186167  217.109365  3732.114787   \n",
       "4           4  100.0  500.0     1.0  0.146088   16.717367  2534.551236   \n",
       "\n",
       "       NPor     NPerm  \n",
       "0 -0.749088 -0.767247  \n",
       "1  0.653263  0.017030  \n",
       "2  0.611663  0.336607  \n",
       "3  1.993601  1.211919  \n",
       "4  0.628172  0.279461  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df['Porosity'], facecolor='red',bins=np.linspace(0.0,0.25,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)  \n",
    "plt.hist(df['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Nscore Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['Perm'], facecolor='red',bins=np.linspace(0.0,1000.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.0,1000.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['NPerm'], facecolor='blue',bins=np.linspace(-3.0,3.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Permeability (mD)'); plt.ylabel('Frequency'); plt.title('Nscore Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity and permeability to standard normal.\n",
    "\n",
    "#### Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the quantitative methods later.\n",
    "\n",
    "Let's plot the location maps of normal score transforms of porosity and permeability for all facies. We will also include a cross plot of the nscore permeability vs. porosity colored by facies to aid with comparison in spatial features between the porosity and permeability data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6AAAAEjCAYAAAAsfmzuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydZXhURxuG79nsxt1dCBIcgmtxd4oUdy/QUrS4U4q0xd2LFS+UUloKFCvuEhwCcffdPd+PXUJChASC9OPcXHuxmTPyHnt29B0hSRIyMjIyMjIyMjIyMjIyMu8axYc2QEZGRkZGRkZGRkZGRubTQG6AysjIyMjIyMjIyMjIyLwX5AaojIyMjIyMjIyMjIyMzHtBboDKyMjIyMjIyMjIyMjIvBfkBqiMjIyMjIyMjIyMjIzMe0FugMrIyMjIyMjIyMjIyMi8F+QGqEyOEEKMEUKs+NB2vEAI8UAIUUf/faIQYsN7KneJEGLc+yhLRkbm40MI0U0IcTyb4weEEF0ziyuEiBVC5Hsfdr6OTGyThBD530O5nvrrYPCuy5KRkfnv8P+irR8jQoiOQojfP7QdafkkG6D6xkuQEMIsTVgvIcSRD2hWtuhtTtC/ZEFCiNVCCPP3Vb4kSdMlSeqlt8VbX1lRvssyhRBm+vPd/xZ51BBCaPX5vPjsfdP8JEnqJ0nSlDdNLyPzrpB17eNAkqSGkiStzeKYuSRJ9wCEEGuEEFPftT36DjpJCFH+LfI4IoRIfEVHK71JXpIkPdJfB82b2iMj8z6RtfXj4GPT1tzwioaGCiF2CCFc3lf5kiRtlCSpXhp73kuHY3Z8kg1QPUpgyIco+C0abk0lSTIH/IFywNj3VO6H4nMgCaj3li9qoF6cXnya5pF9MjIfG5+irgkhxKf8W5YlQggBdAbCga5vmd2gV3T05NtbKCPzn0HWVpm3ZZD+fhQErIF5uUn8/3Y//m9O5A2YDXwjhLB+9YD+Js8TQgQLIaKEEJeFEMX0x0yEEHOEEA/1x44LIUz0x5oJIa4JISL1vR2F0+T5QAgxUghxGYgTQiiFEBWFECf08S8JIWrkxHBJkp4CB4AXNuW23OzijxRCPBVCxAghbgkhauvD005zPar/P1Lfm/OZECJcCFE8TT6O+t43h5ycUxZ0BZYAl4GOb5FPBoQQjYUQF4QQ0UKIx0KIia8cr5rm3jwWQnTTh6frWRNCNBFCXNTHOyGEKJHmWKbXUkbmHfL/pGtZ5qO3Y5oQ4h8gHsgndD26A4QQd/Tv3BQhhK8Q4qT+Pd8qhDBMk0d27+4oIcRdfT7XhRAtM7mcP+mv1c2077betl6ZnaPexvxCiD7oNG2EXkP3CiGGCyF+eSX+T0KI+Tm5fllQDXBFV3Fun/b88wIhxDYhxHP9dTgqhCia5limz5R4ZQaNEMJKCLFSCPFMr5dThX56rv5a/a1PHyqE2JKX9svI5AJZWz8BbdXbt/2VsB+EED/qv3cTQtzT239fCJHruqkkSeHAL7y8H5WFEP/qz/lfIUTlV8751fuRXfxM7RNppiwLIV7U4S/pr1E7IcRVIUTTNPmo9JpbKrfnl5sL8cl9gAdAHWAHMFUf1gs4ov9eHziHrodCAIUBF/2xhcARwA0wACoDRuh6NOKAuoAKGAEEAIZpyrwIeAAm+vRhQCN0HQF19X87ZGez/rsHcA2Y8gblZhkfKAQ8Blz1ab0BX/33icCGNOESoExj3yJgVpq/hwB73+IeeQJaoAgwDLiczfVItS2TfGoAT7IIL66/9iWAIKBFmrJjgC/018gOKKU/tibNM+MPBAMV9M9CV71dRtldS/kjf97Fh/8vXcs2H72tj4Ci6EYmVOg0aQ9gqQ9PAg4D+QAr4DrQVZ8+y3dXf7wNuoabAminvwYvrlU3QA18pS+3HRAF2KaxrVeauMfTnK8E5Nd/X/PiPun/dtGXY63/W6m3scxbPBMrga16O8OAVmmOZWlbJvmkntMr4T0AC/2zMh+4mOZYVs+UN2l+P4BdwFLADHAEzgB99cd+Br7V3wdjoOqHfs/kz6f3QdbWT0ZbAS90DT1L/d8GwDOgIjqNigYKpcm3aA6fobS22wN/AusBWyAC3UwVJbp6ZwRgl8X9cMoqfnb2ZXe99H+PALak+bs5cOWdvlcf+sX+EB9eikkx/cPtQHoxqQXc1j9wijTpFEACUDKTPMcBW1+J+xSokabMHmmOjwTWv5LHQfQvcRY2xwKRwEN0DT6TNyg3y/hAfv1LWQdQvVL+RLJvgFZA1+BS6P8+C7R9i3s0Fn1lBp1YaYDSr97DV23LJJ8a6BqykWk+GexCV3map/8+GtiZRX5rePkDtBiY8srxW8Bn2V1L+SN/3sWH/y9dyzYfdD/Kk185LgFV0vx9DhiZ5u85wHz99yzf3SzsvAg013/vBgQCIs3xM0DnNLblupKkDzsA9NZ/bwJcf4vnwRRdZaSF/u+lwO40x7O0LZO8jqCrlEXqP+cziWOtz8PqNc+Utz7ei8pUEmCS5vgXwF/67+uAZYD7h36/5M+n+0HW1k9KW4HjQBf997rAXf13M/31bJ1Ws3L4DB3hpYY+BTbqn6POwJlX4p4EumV2P7KLn5192V0v/d+u6AZeXjS8twMj3uV79SlPwUWSpKvAPmDUK+F/AgvQ9VwFCSGWCSEs0fVaGAN3M8nOFd1L/iIPLboGmVuaOI/TfPcC2uinKEQKISKBquh6LLKihSRJ1pIkeUmSNECSpIQ3KDfL+JIkBQBD0TXogoUQm4UQrtnYk4okSafR9TB9JoTwQ9cA25NZXP2UkxeOLKplkWUXdC8okiQFAn/z5muYAvXX7cVnqxCighDiLyFEiBAiCuiH7v6Crrcws3v8Kl7AsFfuoQe6Uc83vpYyMm/D/4mu5SSfx5nkFZTme0Imf79wwpHluwsghOiSZgpZJLqKp32avJ5K+l9pPQ9fpH1L1gKd9N87oeshz4DQeTR8oaEHssirJbrRhBdO3DYCDcWbL4sYnEZD/YUQBkKImfrpdNHoKrygu07ZPVNp8UI30vEszbVeim4kFHS98gI4o//d6PGGtsvIvDWytqbyf6utejah6wgD6KD/G0mS4tCNyvZDp1m/6uu7OeWFhrpJktRRkqQQXnkO9Dwkh3X4tPHfxj59PfsfoLXQTTNviL4O/q74pBugeiYAvUl/s5Ek6UdJksqgG/YuCAwHQoFEwDeTfALRvXhAqvMHD3Q9HanZpvn+GF0vVNqGkZkkSTNzaX9uy802viRJmyRJqqqPIwGzMilTyiQMXr7gnYHtkiQlZhZJkqSi0ktHFsdePa6fz14AGC1064ueoxth/ULknSOlTegayB6SJFmhW2sq9Mcek/k9fpXHwLRX7qGpJEk/Q46vpYzMu+C/rms5yScrHcpp/pm+u0IIL2A5MAjdNChr4Cov9QHATX8tXuCJ7lrlhszs3wWUELr1Y03IogIg6TwavtDQhlnk3xVdpfCRXkO3oWvsfZFF/NzSAd00rTroRj299eGC7J+ptDxGNwJqn+Y+WEqSVBRAkqTnkiT1liTJFegLLBIf2HOjzCePrK2vz/8/q616tgE1hBDu6DryNqVmLEkHJUmqi67BflN/Pm9DuudAjyc5rMO/Gv8t7XtRh28DnJR0a4ffGZ98A1Q/UrUFGPwiTAhRTj9CpkI3qpcIaPQ9VKuAuUIIV30PcCUhhBG6dTaNhRC19emGofthPZFF0RuApkKI+vp8jIVuyxD3XJ5CbsvNMr4QopAQopb+fBLR9Wpl5io/BN201lf3XFqP7mXthG7q1JvSFTiEbv1nKf2nGLopZVlVtnKLBRAuSVKi0G1P0CHNsY1AHSFEW6Fb+G+XxULs5UA//bMihG7bmMZCCItcXEsZmTzn/0DX8iqfrMjy3UU3jUlCp3MIIbqjdxaRBkdgsNA5amiDbs1XbreLCuIVDdV32m1HV+E5I0nSo9yemN5mN6A2uopWKf2nJLpOsDedSfIqFuiehTB02jz9xYHXPFOkifcM+B2YI4SwFEIohM65yWf682iT5p5HoLsvso7KfDBkbX0t/3lt1Y9MHgFWA/clSbqht9dJ6BxHmaG7V7G8vR7tBwoKITro65vt0NV99+U2fi7ty3CN0DXS/dH5cHmbOnyO+OQboHomo3sxXmCJ7iWKQDe0HQZ8rz/2DXAF+Beda/tZ6Ob830LX8PoJXa9XU3QusJMzK1CSpMfoeo/HoHsZH6PrMcvVPXmDcrOLbwTM1Ic/RycEYzLJIx6YBvwjdNMoKurDnwDn0QlMhpHNnCCEMAbaAj/pe79ffO6ja+DmVeVpADBZCBEDjEf3YwDo9qlDt0B/GLp7fBFd5S0dkiSdRdcTugDdsxKAbp495PBaysi8Q/7LupYn+WSTf5bvriRJ19GtaTqJ7ke6OLqpSWk5jW6WRig6LfxckqSwXJqxEiii19BdacLX6svMborY6+iMbg3972l1FPiRl6MAb8s6dM/RU3ROSE69cjzTZyqTfLqgc4J3Hd292M7L6YDlgNNCiFh0M1aG6H8LZGQ+JLK2Zp3//4u2bkI3u2NTmjAFunphILp7+Rm6uiRCiGp6ncoV+nNros83DN2ygyaSJIW+Qfws7cuEicBa/TVqq887AZ13Xh90DrfeKUJKN9VaRubtEEKsQrfmMlf7TcnIyMjIgBDCE93UKWdJkqI/tD0yMjIy/w/I2vp6hBDjgYKSJHV6beS3JK/W08nIIITwBloBpT+wKTIyMjL/OYRuk/Gvgc1yBUlGRkYmb5C19fUIIWyBnuhm0Lxz3tkUXCHEKqHblPdqmjBbIcQhodvM9pAQwibNsdFCiAAhxC0hRP004WWEEFf0x34UIt3iZJmPBCHEFHSLyWfLU6T+W+jXgJwRug2prwkhJmUSR+jfvwCh22TbP82xBvr3NkAIMSpNeJbv+6eGrIcyr0O/bicandv/CR/YnE8WWQ//W+Tkfsl82sja+nqEEL3RTcc+IEnS0fdR5rtcA7oGaPBK2CjgsCRJBdBtYjsKQAhRBGiPzntYA3Se7gz0aRYDfdDNCS+QSZ4yHwGSJI3Te2Sc9qFtkck1SUAtSZJKonNW0uDFut40NOTlO9gH3XuJ/j1dqD9eBJ2n4iL6NJm+758oa5D1UCYbJEmK02toUf06LZkPg6yH/y1ycr9kPmFkbX09kiQt13tD7ve+ynxnDVB9Czr8leDm6BYBo/+/RZrwzZIkJelHzwKA8kIIF3Sbop6UdItV16VJIyMjkwdIOl4snlfpP68uDm8OrNPHPQVY69/P8kCAJEn39M4TNuvjvkiT2fv+ySHroYzMfwNZD/9b5PB+ycjIfGS87zWgTnq360iS9EwI8WKzaTfSe9B7og9L0X9/NTxThBB90PVGYmZmVsbPLzf7w+YNKSkpBN57hIOxZfpwjZoYpRp3T4/3bpNM7tFoNDy+ew9nk/T3Ua3VEqpOwEAr4WBsnu5YkkZNnBJc3bN8RLPl3LlzoZIkvXaT+Lr1HaWw0Ewd5WXKhfNR19C5hX/BMkmSlqWNo++5PwfkBxZKknT6lWzcSL8Z8ot3MbPwCvrvWb3vMjo+DT28/whHY4v04Ro10QYaWQ//I2g0Gh4F3MfJ2CpduFqrIVwbj0INdkbptTJZnUKCsVbWQ1kP3zk5uF/p9NDY2LiMp6fn+zUyG7RaLQrFx7MphWxP9vw/2BMfH4+JqSEC8Up4Eqampm9lz+3bt3Ok3R+LE6LM1jFJ2YRniv5HZBlA2bJlpbNnz+aNdbkgPj6ejnVbML5IK9Iuzzr99CaJNTwYMvzr927T+yYhIYGQkBCcnJwwMjJ6fYIcEBERQWxsLG5ubu/lxU9JSaFt7QZM92+MQZryzgfe55a3OddOnGFKmUbp7vFfD65jUMefXgPebAaDEOJhTuKFhaZw9ETNHOdrYbwrUZKkstnFkSRJA5QSQlgDO4UQxSRJupomSp68ozI54v9KDzvVa87kEs3TvSunnt4iroo3g2U9fCM+hB5+XrMR4/1apdPDi0F3eeBnyJVj5xhZoE26e3ws8BIWLQrTe0DfNypT1kOZnJKD+5VODwsVKiTdunXr/RuaBUeOHKFGjRof2oxUZHuy5//BnipVyjNseBWat3gphadOBdCl4ypu3ryZTstzS061+3034YP001TQ/x+sD38CpO0Kd0e3j80T/fdXwz9aTE1NqVS/Brvun0at1e3/+iw2nN8ir9GmY/sPbN27RZIkFs2fR9cmDfhhSD86Na7PmuXLeJutfqKjo/m63wAGtWrH9F4DadegESdPZLVPc96hUqmo26o5m26eIVmjBiA4NopfAq/RtV9viletwO6Ai2i0WgAeR4Xxe9h9WrT9/J3bBoAkcv7JTbaSFIluA+ZX1xZm945mFg5Zv+8yOj4JPaxYvwY77v77Ug9jwvk19Aaffwp6+MM8ujZtwI9D+9I5j/RwWL8BfNm6LTN6D3iveli/dVO23TtBil4PQ+Kj2Bt6kW79e1GielkOPD6dqodPY0I4Gn+Tlm1bv3PbAFkPZYBs75eMjEwavv12Il8O3MDeveeJjU3kzz+v0a3zcr4dO/6tGp+54X2PgO4BugIz9f/vThO+SQgxF3BFt7D/jCRJGiFEjH5B+Wl0G1b/9J5tzjXDvh3JYvMFTN6xE6UksHS2Y/KSObi6un5o094pmzduIOT4IRY1qoxCIdBotczet439zi40btr0jfIcN2w45eMFlSvUBSAqMZ5po8byw+YN7/x69hs8iJWGKsZu+QWVFkztbRg1dybe3t6MmjSBhXPnM2rfr6gQ2Lg5MX3ZAuzt7d+pTYCuPz0P+9SFEA5AiiRJkUIIE3SbL896JdoeYJAQYjO6KWVR+mlkIUABIYQPug3p2wMd0qTJ7H2X0fFp6OGYUSz5YQHjd+7R6aGTPZMWz/2/18MtGzcQevx3ljSulKqH3/36dno4fthwKiRA5Yp1AIhKiGfq6LF4/fwe9HDIIFYaLmXqlp0YaMDM3poxP0zH29ubMVPGs2DOD0zbsxkDSYGdhyOzVv4g66Gsh++cHN4vGRmZNDRq1IilS1czZcp4OrRbRIECPkyYOJNOHd/59p+pvLMGqBDiZ6AGYC+EeILO9fFMYKsQoifwCGgDIEnSNSHEVuA6oAYG6qdUAPRH50HSBDig/3w0aDQaNq7bxJ5Ne0lJUVO7SS16DejBoK+HMPCrwWi1WgwMDF6f0UdCfHw8q5Yu4/jvh1AqVTT8vBVfdO6Uo2le+7ZsYmaVoigUut4TA4WCvuWKMX39mjeqcAUHBxN57yGVy9VODbMyNqWpqy+7tm1nwJDBuc7zdURHR7Ni4RJO/nkUI2NjWnRuyy9/HgRIdx9VKhVDRw5nyIhv0Gq1PH/+nMVzFnL1/BVs7G3pNrA7n9Wskef2vUBo87SHygVYq19HowC2SpK0TwjRD0CSpCXAfqAROoc48UB3/TG1EGIQcBAwAFZJknRNn2+m7/unyKekh5vWbWTflj2oU9TUbFybHv16MvDrIQz4j+rh6mXLOP777yhVKhq0bs0XnXKmh3u3bOK7akXS6WG/8kWZuuHN9TDi3gMqV6iVGmZlYkpTt3zs3rad/u9MD5dy4vDfGJuY0KJTW3b8pXvkXtXDr0Z9w9CRw1L1cOH3i7hy9iq29jZ0H9yNGjVr5Ll9L5D18JMm0/v1gW2SkfnoadKkCU2aNPlg5b+zBqgkSV9kcah2ZoH67TsybOEhSdJZoFgempanjPtmAsGHYqhv8QVKoeTi2jP0+ac/635ZjUKh+E9VtrRaLYO696ScxohJxaqSotGwc/s+Jl+/wcRZM16bXp2UhKmhKl2YlYkxsdFvtudvdHQ0tkYmGcLtTc15EhLyRnlmR3JyMv06dqey1pVR3o1IUCezc/EOHt65z7BvR2aaRghBWFgY/dv1oZ6yMjVsOhEWF8GS4T8ROTqS5q1b5LmdACIPe/wlSboMlM4kfEma7xIwMIv0+9FVyF4NDyOL9/1T41PRwwkjxhF3PIzOjs1QGik5tf08/f/py+pta/+Tevhljx5UMjBgZrlypGg0bNu1i8nXrzNx5szXpn8XemhnnIkempjx+F3pYYceVFS7McKjKQnqJHYv2snDgHuv1cM+n/ejcnJNOln0JiIonJ+GLCZyQhQtWjfPNN3bIuvhp0tW90tGRubj5uNx4/QBkCSJf//9l9lT57JiyUpu3rzJmpVr+H7qHE6ePIlWv54lK548ecK1I7f4zLohxgYmKBUqylpVQdwz5tSpU9mm/VCEh4ezYc1a5s2YxfHjx9Od44kTJ3CJTaFB/qIYGigxMzSiU9Fy3P/3AoGBr19q5uNXhCtPg9KFnXrwmFIV3mxLLm9vbx4mxBCfnN7D4T/PH1GhxmdvlGd2/PH77+RPNKeaezGUCgMsDE3oXKAGJ/f/SURERJbpNqxcT1XhT2HbAgghsDexpZNrC1b9sPy1z9AbIQHaXHxkZHLACz2cM20OK5fq9HDtyjXMnfZ9jvXw9vEbNHGtjYnSGJVCSTWn8pg+Nfi49XDtWubPmpmpHrolJtLYzw9DpRIzIyO6lS7Nw3Pn3lgPT95/Wz2MzUQPH78jPTxEvgQLqroV1+uhKR19a3Li17+y1cP1Kzbgn1iRApaFEEJga2RHC+u2LJ+7QtZDGRkZGRngE26AarVaRg4Zw8zuiwnaKDgx8x6N/FtxeOZREnZILBqwnK/6fp3tD2ZAQADOmozbCLioPbl57ePxsPaCK1eu0LtVG2L3/YXXjUfsnTaHr/sNQKPRze67dfUahcxtMqTzs7QlICDgtfkPHD6SxVcf8eu1AAKCw9h15TbrA4LpOfDLN7JXqVQycMxIpp77izOP73MnNIhVV04T42pHjZo593qYU25evk4BM+d0YUII8pk68PBh1k69bly8Tn5Lr3RhxkojDFNUxMfH57mdoOvxz+lHRuZ1aLVaRg8dzU/9f0K9R83NBTdoUaE5/8w/jMHBRFYNXczX/b96rR56GmRch+ht4MatazffpflvxJUrV+jzeWuSDv1G/nsBHJg9i2H9+7/Uw2vXKGJllSFdERubHOvhwiuP+fXaXQKCw9l5+Q7rAkLoOeDN9XDA6JFMOXskVQ9XXj5NjKv9O9LDaxQwzaiHPq/Rw+sXruNl6pMuzMjAGFWSoayHMjIyMjLAJ9wAPXHiBPcPh1DbtD35LIpiE+FOO4OvuRUcQAHLQjS1bk3MmQQO/3E4yzy8vLwIVT7LEB6qfEa+Aj6ZpPhwSJLEzDFjGVGsEg0KFMHfzYv+pSphFRjKwQO6NT3e+X25H59xetjd2Ei8vLwyhL+Kh4cHSzZvI6VCbX5NMUNZrTHLt2zH0fHNtzurXbcu01Yv40lRD45bC6p+2Yt5Sxe/k6l8+fwK8CA+41S2x4nhuLllvZedb+H8PIp9mi4sWZNCWFIEAQEBpKSk5KmdQgKhkXL8kZF5HSdOnOD58ed8bt+awjZ+uMa5MthmIDef36WwTQHauTQl+WLMa/XwmTajY8+n2mB8CuR7l+bnGkmS+G7st4wt50/jwn6U9fTgy/JlsQsJeqmHvr7cjY3NkDYgOjrnevjzNpLK1WZvkhnKqo1Zvvnt9XDqquU8KuzFUUsDqgzqw9wl70oPC/IgIaMePnmNHuYvkp+nCU/ShaVokolQh8t6KCMjIyMDfMIN0D/2/oWvftlASkoKQiMwV1hhqbUnJFFXiSpmVJpDu//IMg8fHx9cSzlwJuooaq0araTletQFol1CqFat2ns5j5wSFhaGUUIyThbpNwuv6Z6Pvw/onOx8VqMGt0QSJx7eRStpSdFo2HP7CjaF8uWowgVgY2NDj959mfT9XLp0746lpeXrE72GfPnyMWzMaCZ/P5v6DRq8s3Vk9Rs24BLBXAy6iyRJpGjU7Lp3ikKVS+PgkPWeup16deZw0imexOim5T2KfsqI41OwiDdhycD5tKrRnKNHjuatsVIuPjIyr+GvX/+kpFEJQKeHCq3AUmmJnbAnSN8pU8a8OIf3HsoyDx8fH+yLO3Mk6GSqHp4PvUqQTeRHqYfGSUk4v6JPtb09Ofrbb4BOD2+o1Ry/fx+tVqeHO69fxzJ//jfSw87/OT2szxURxKXggFQ93P3gBIUql8pWDzv37sQpxd8Exus65Z7GPWHKpbGYRJszv9cimldvKeuhjIyMzCfO+96G5aPB0saCSG0cAAqhQKv/ZUoiASOFbrPwBE08ltbZVxi+XzybhfMWs33PMrRqLeUalmHFmKUfnbMNIyMj4tUZe55jk5Iwd9ado1KpZOHa1SyYPYcdJw8hDAz4rGF9pn056H2b+0EwMTFh0YZV/DhzDrvO7kShVFK/VRN69OudbTp3d3fmrPuB+VPmsj3gIAH37tI3X0eKuRdGAHEp8Xw3fDp+v/q91ehHOuSKlEweYm5tQbxar4cKBVpJN9U2QUrAyECnh/EpCVi8Rg+/W/A9i39YxKJ9m9BqtJSpWpalo5d/lHoYl8lIXExSEmZWdoBOD39as4YF33/PlqNHURgYUL1BA6YN+oT0cONKfpw5h93/bsdAlXM9/GHTPOZOmsfBO7u5e+8uXzj3pLBLUQQQr45n+tez8PtN1kMZGRmZvObKlSvs378fU1NT2rRpg7Oz8+sTfQA+2QZoy3bN6bfpK7w1fhgqjTE0UXEv9jqG5gJrIxtStCmcVh9jVqfp2eZjZGTE16OG8vWooW9lz95de9i0dDWJsfE4e7oxaMwwihYt+lZ5psXCwgKPIn6cenyfih666cHJGjXb719n2NdzU+PZ2NgwbvrUTPN49uwZC777jtuXL6FQKanXohXdevf+6CqXb4OjoyNT5+Z+C7GCBQuyaP0Sbt++zYyuEynuXDj1mJnKlAqqkuzf8yvdenXPEzuF7ExDJg9p0bYFQ7YOoaCmIEYGRqhMDLkRfQulkcDW2JpkTQpH4k8xtUP274aRkRFDR3zF0BFfvZU9e3ftZvPKlSTFxuHk4cGAkcPzXA/dChfmxIOHVPbWjWYmq9VsvhXAV/NeNjBtbGwYNy2DM2JAp4cLZ8/i1uVLGChV1JX1MJWCBQuyZONibt++zYQvplHE4uW9M1WaUlxTll/37Kd7r255YqeshzIyMp86kiQxYuQ3bNywjjZtyxIVlcSECWNZsXI1rVq2+tDmZeCTbYD6+PgwdHpf5k5ciF2SO3HOUdyLuIGvvQ+/xezhmeIJvcf2onDhwq/P7C3Z/vNWDi3czEDfGpi7m/AkOoRxfb5i7sZleHt751k542ZOZ8yQr/jj3yM4GJtyJzaCL/r3pXjx4q9NGxcXx9Du3eju68HgOlVJ1mjY8udBvnv2jNGTJuWZjf914uLiMFNk3CrBVGFEbExc3hTywuujjEwe4ePjQ/9J/flpyk+44kq0WTS3Em6S396bHSG/8UgTSM+Rvd+PHm7ewtFVaxlTqjwWRsY8ighn4sAvmb1mVd7q4fQZfPvVUA4eO4GjqQk3I6No369/zvWwRzd6FnRlSIPKJGs0bP77AN89D2T0xMl5ZuN/nbi4OExERj00wZjY6Izra98IWQ9lZGRk+Pvvv9m5YwuXr03DxsYMgPPna1G/Tg/q1qmLhYXFB7YwPZ9sAxSgYZOG1K5Xm5s3b2JpaYm3tzePHj0iMjKSggULYmxs/M5tkCSJjctWM7JAY4yVhtwOe8I/j66gjE1kyrcTWLFhDULkzSbblpaWLFi9kidPnhAeHo6XlxdHjxxh7NBh2Ls40+qLdnh6egKQkJDAvt27ufLvKZw9vVAZmVDdzgp/D53zCSOlks6li/PN4eOEh4dja2v7VrY9fvyYXVt+JuR5IGWqVKdh4yYYGhq+9Tm/b4oWLcoDbSAJ6kRMlLrnR5IkziddZ0y9dnlWTp5uuy4jw8ejh5tXrGR62WoYq1TcCHrGkTs3MI2OZeq4cSxfty5P9fCnlasy6OG4r7/C3tmFlu3bZ9TDs6dw9tDpYQ17C/w9X+phlzLF+Pr3PNbDZ88oU7U6DRs3/s/q4TODxyRqEjE2eKmH18UlJtUfl2flyHooIyPzqbNt+xZ6962e2vgE8Pf3oULFAhw8eJDPP//8A1qXkU/WCdELDA0NKVGiRGrPuqenJyVKlHgvlS3QOfxQpoCx0pADd06z/eIhShs60sy+KFGnbzPl2/F5Xqa7uzt+fn58038gl1b8TI1YAxwv3eObTt05cfwfYmNj6dfpC0IObKaZcQLOt86wacF8DKX03cxCCLyszHn2LKMn4Nxw5vRpRnTviNfDCzQziePBzjUM6NaJpKSkt8r3Q2BoaMjQiV+z7PlmTgVd4HLoddYE/kKJJmXzdAqhvO+dzLvgY9BDQ60WY5WKXVcusPHk31SwsqKddz4SL11l2ri8a7S84IUeDh84gOsb1tCAFDwCrjOiWxdO/KPXw87tCT34M81NY3EJOMWmhXMxFBn10NvKLG/0sFsnPO5dpolhPPe2r2VA187/WT38evJQNseu5nz4v9yIvMovURsp06KUrIcyMjIyeYgkZe5pTYgXxz4uPukR0I8BlUoFJgYExUZw9P4FxhVuilJhQFxKIn2L1mHLP5e4fv06RYoUydNy9+/dR75YDe2KlAUgn50jhR1cmDFlGnVbNKGmtQEtShQEIL+jLc7GSsbuO07zEi8rDZIkERAZjYdHxr1Qc4okSfw4bRJTa5fEztw0tbwNZ2+yZ+dO2rRvn6N8oqOjOX/+PGZmZvj7++f5Oqzk5GTOnj2LRqOhXLlyBAYG8uDBA3x8fPDxSb/lTp36dSlSvCi/7tpHXGwcIxqMzdG0vhwjyWueZP4/UalUaAwNeRYdxZ83rzK7ymcoFQrikpMZWrEyK8+cfTd6uG8fhZIT6eRfEoD8DvYUdXZi/NQp1GnejFq2ClqULKA75mSLs7GCsXtO0bzkSzskSSIgIuat9fCHaZOZ/Fnpl3roYMfG8zf+s3pYt35dihYvyr6d+4iLiWdco1GyHsrIyMjkMW0+b0fv3p3o1bsm1ta6UdCLFx9y6uQdNv9c/wNblxG5AfqBEULQc+hA5o6cQjFzZ5QKBQnqJKI0CXg6eFNW48Hpf07keYXrxOG/aOTinS7MytgUK62Co4cOMrVc+n37/DxdSdJqOHjjNrUL5ic2OYl1F69RtXGTt9paICIiAlNtcmpl6wWf5XNlzZHDOapw7d65nc3L51LV15joJJgXrGDq3GXky5c3ew+eP3eeiUO/Jb9wQdJq6H+vJwUd8lHYyod7SYFYFLDnm/EjMTc3x9XVFQBXV1d6D+iTJ+VnzsfXmyUj87YIIeg+eBCTx06gjK29Tg9TUghPTsLT3YUq8bGcPpH3enjqz8O08nJPF2ZtYoKtgGO/H2RKpfTbrhT2ciNJq+bgtTvU9vMlNimJtedvUKVR07fWQzN1Jnro48ranOrhL7+wcdGPVHC2JlatYU6smuk/LcpTPRw3aByeag+0WoneT3qT3zY/BcwK8EjzCJvCNoyYOCKDHvYZKOuhjIyMzLuiRo0aNG/RlhJFv6Vt+/JERSaya+dZli1fmSdbgOU1cgM0j9FoNCgUilytU2rQuCEhYaHsmLKQZ0lRGJsa4+nhhUqlIjwlgaIO9nlup52jAyEBwXha26WGSZJEVHIi7o75CI6Jw8o0zbQ7SeDo7klwoeJ8ffwopqZmNO/ak2YtW76VHaampkQnJiNJUrprFhobh63D60cSHj58yK7Vc1jZqyCGKl0v//3nMUwYPpB12/e/9XqxpKQkJgweTT/n5lgbWbDr9p/UNylHMZEfw2RD8ic6cOjwaVrsaUR+r/xY5bNl2k8zcXV1Ral8d6+XUL+zrGVk8ow30sNGjQgJDeXX7+fxJC4GY1NTPFy9USlVhCYl4WOf93po6+hIcOAjvGxtUsMkSSIyKQkXTw9CYuKxTqOHkiRwcPfkef4SfHXkGCZmZjTv0otmLd5eD6OSMuphSGw8tg6ur03/8OFDti/5kZ8aVcRQqdPDh2GRjPtqMBt27c0TPRw7cCwdTL/A0tCS3x7+RjWpGoXiCmGoMcQ13pVjAcdpuLMpvh4FsStoxaxF02U9lJGRkXnHCCGY8/08unTuxq+//oq3pxlTp6xP7Qg8duwY69avJiE+nkaNmtG2bdt3qsuv45NfA5pX3Llzh97tO9P2s/q0qFaH2VNnkJycnOP0nTp3wqyIB2o7E+wcHAh8EsiZSxfYeP4wl89eylVeOaF1pw7seHST2KTE1LDD92/iV86fDr36sOLCHZLUul91SZL4+eItajRqyrAxY9i8/zdWbf+F5q1avXWFxtjYmGIVq7L36r3UOepxScmsu/yQ1h07vzb9b/t20basVWrjE8DH2QI3swTu3LnzVrYBnD59Gj8Dd6yNdN7DLjy7Tg3bMhhqDJDi1WhTtLSwq4GTgS2N1VUxva2gnn9tWlVuzud1W/H3X0fe2gYZmf8aOj3sRLsa9WhZvTbfT5ueSz3sjGF+HxKtLLBzsOfZkyecvniR1f8c4+r5C3muh606dGTznXvEpllnefB2AAXLlKVDr76sOBtAUkoaPTx/mxoNmzJszLf8vP83Vm37heYt80YPi1eqwr7rd9Pp4fqr93Okhwf27qZFfpfUxieAl501Lgp1numhj8YHS0Ndb/qVkCtUMquIMkWJJk6LNkVLXZMG2EkOVAipi/aiETWK16NJuVa0qPk5R/76+61tkJGRkZHJmpIlSzJmzBiGDBmS2vicOWsGnTu3pZBfMp/VtOCnBVNp2bIpGo3mg9kpj4DmAeHh4YzqPZC+XpXw8C+PVtJy4NRlpn47nsmzZ+YoDyEEc5cvZMSAIdz5bQeOKhsSJInh/gN58PdTJo4Yz/T5OcsrJ+TPn5/e40YxaeZsnJRGRCQl4FO6BOOmTMLExITA3l8yaPFPeJgb8yw2gZJVP2PYV1/nWflp+WbseGZOHM+gfadxMDMmMF5N769H4+fn99q06pQUjFQZ+1GMlArU6rfvFler1SjTvCaSpLtXSBISYCKMMVIYokXiRmwAYYkRDLbugYulGxoDLT98MxfrVTaULFnyrW15aQSyMw2Zj5bw8HBG9RnIwHzl8Czvj1bSsv/fq7nWw++XLGHUl19y/+AB3EzMSZDg+8ZtuXX9LlPGfMuU72fnmc358+enx+hvGfXdLFyMDAlLSMC7RCnGTpqs08Negxm45Cc8LYx4FpNAiSqfMeyrYXlWflpe6OHg307iYGpMYGIKvb/JmR5qUlIwyqRH29DAIO/0UHrZuJUkEJJCp0dCwkgyQSUMkdByL/kWoQmRtFV9iavGFSlGw3eDf8Bmg7WshzIyMjLviWfPnjFr5gwuX5uOq6tulk/nLlWpVmU6u3fvplWrD7NH6CfTAA0PD2fTmg1cOn0Rj3yedOrdJdM1MdHR0cyfMY8zf58CBJXrVGHIyKGYmZllzFTPnl92UsPSGw8r3dQwIRRUtPLgm00bufDPKdzy++BTqCD3rt7G0cWJ9j07Z+oB0NHRkRoN6uHy1IZy9sWwNbZGCIGPlQc/nd70Ru79AwMD+XHWdO7duIowMKBOs1b06NMPAwMDatWtQ43atQgMDMTS0jLdHPFmrVrRsGlTnj9/jpmZGRtXr+CLxnV4HhSCoUpFqTLlad+zN+XLl8+VPZlhbGzMxJnfER0dTVRUFK6urlk6zbh27RqLZs0i+MkTVCYmOOTLx5bD1yjh8YDqpZ1pWsGTqPhkboVIWVbYIiIi+GHmbC6d/BcUgip1azHw6yGYmGTcr658+fLMS5lFHXU5jJVGFHbIx9nI67ga2GNpZI6QBP9GX6egqQ9no6/R06EjWoUWjUaDhakljazqsPqnlcxf8eNbX6d0yBUumbdAp4cbuXjqIh75POjc5zV6eOQ0CHKsh3WsPVKn9wuhoJK1G0M3b+HiyRO4+ebDp2BB7t+4hYOzE+26d81SDz+rX5+iiYLqXr44mFkghKCggzPfnvrjLfRwGvdvXkUYKKnTtBXdX+hhnTrUqJVzPWzfuC5Bz4MxMlRRsmx52vf4MHq4cNZ3BD/W6aGjrw+b9p+kqONNqvh50rB4AaITkgiITcpeD2fM5uKpf0EIqtatxcBhWevh9+J7qmqqYmRgRAHbAlyMvoiz5Iy5whwJBVeSLuGlLMD1pAs0VfVCQkKj0WCptKBKSiNWzF/NT6vnv/V1SoeshzIyMjKZ8tdff1GrdvHUxieASqWkU+eK/Hbw1w/WAP0kpuCGhobSo1VXYnYG0ji+MvanlXzVfhDnz51PF0+r1TKwS38Mj6kZ7NCNwQ5d0R6O5cvuA7N1YfzkwUM8zF9WhEKCgkgIj6SUjSu9fMtx9+g5AtefoKWmCIXvGjGl13D++P1Q5nnde4yvhQd2JjbppnPZK20IDg7O1XnHxMTwVc+uNDSJYVmzcixoUIr4U78xa/LE1DgKhQJ3d/dMFyirVCo8PDyYOXEsJjePYRUfRlsvK2aV96JK/ENWTxjB5g3rc2VTdlhaWuLh4ZFlZevhw4dMHjSILva2/FSrBsWFxPO//mBwCX8a2hbgyqko2kw9wuANDxgx6XsUioyPt0aj4ctuvfAKiGFayaZMKdYYw9P3GDFoSKZlmpubM3jiMH58tI0/n5zB1sKazeGH2Rn2N6cjrrLq2R5+CfmTwORnhCaHk6BJJEYdh6GREQCups48efgkz64R6Pa8E7n4JyOTltDQULq17EbY1mBqR3yGxTEThrT9Mks9VB1TM8ihG4Psu6H54/V6+PTBAzwsXv7QhQQFkRgRib+dMwOLlOL+iTNE7jxMV3MPyoeomTFgKIez0MOn9x9S2N4JR3PLdHroYmrxRnr4da8uNLGIZEVrfxY1LUbCv/v5bsrE1Dg50sMJYzG+eRzr+FDa+Vowu7I71ZPusWbScLa8Zz2cOPBLOlg6MbdqXYpqFDw9eIQBhctTz7YA168G0WX5Tkb9eZ5R02ZlqYeDuvbG41Y8U4q2YFLhZihPPmDEwKGZlmlubs7QKUNZHbGGf0JOYGtmw69Jv3Iw4SAX4y+yPXYLB+L2E6x+SoQmjCRtInGaGIz0e5g6GLvyWNZDGRkZmfeGpaUlISExGcJDQmKwtLR+/wbp+SQaoGuWrKKqVIKKTqWxMrKgsG0BOjk0Zd7k79PFO3v2LGZBhpS1L4FCKFAIBRUd/OGxmqtXr2aZf6kKZbkU8RQAjVbDpUcBnA19yOXwp1x/9oSqloVp5FQa4tUUsvNkQP7GLJo5D602Y7dtqUr+3Iy7ny5MrdXwNCUYLy+vDPGzY//ePdRzM6OUhxNCCAyVBnQt58fN08eIiIjIUR6PHj0i9uFNnMwNyWduTMdS+chnb0ExBwuGVSzIjjUrSEhIyJVdb8qmVSvpXCA/Xra2RCcmcvb+fb6rURU3ExNK+BWhQ7nKeFl60n/ENPz9y2Sax/Hjx/FMNKSiW0GEEBgoFNT1Lk7yg6BM10ip1WoMjY1o0ac91q0L4t6pNAWK+qCyS+T3yCMkGoSSz8qEaCkYlUKQpI7HXBjz+N5DEhMTuBF5i1IVSufthXgx5Uze907mDVi9ZDUVkvwpa+ePpaEFhawL0saqFXMnzUkX7+zZs5gGGVLGrmSqHlZwKIP0KHs9LFmhHBfDAgHQaLVcfHiXMyEPuRQWyJVnT6njnI+WPoURiSkUcXJjROkaLJk9J1M9LFm+LBdC0++rqdZquBcT+UZ6WN/DlFJeL/WwW8WCb6CHt3AyM8LX0ohOpXzwsbOgqIMFwysX4Je1y9+bHm5cuYoOPoXwtNHr4YN7TKlQCxcjM0r4FaGdf1W87dwZOHYS/mWy1kOPBCMqpNHDOl4lSLyftR4amRjRZkAb3Lu4U6ifHwWL5UNlk8yR+D+IIxIXlT3RhGGAgkRNPCaSKQ/vPSYhMZG7sTfwr1gqby+ErIcyMjKfIJIksWz5Uq5fv4a1tSX16tXkxIkTGeLVrVuXuwHB7Nr5b2rYrVuBrFj2N127dHuPFqfnk2iAnvvnX4rbpp9+ZG9iS+Tz8HQ9+Y8ePcJJY/dqclwkBx4/fpxl/vUaNOCeSTK775xlyl8buRp3HaX5c4o7m7H98kmKWnpipFSSnKhz+GOmMsZcY0h4eHjGvOrXI8gxlr8CTxGTHEdgbBBrHu2kbd8OmU6Jyo6HAbfJb2+VLkwIgY91zjdLf/z4MQVszbj6MJgK7i+9TxoZKJDUagrbWXDv3r1c2fWmPLwTQH69R+C7oaGUcLDDQKHr01YqVdja2lEnfz5uXbmSdR737+NpZJUh3MvIOsM9DgwM5ItGrdg/aRWhG87y77Y/2LHuZ9o5+6FJ0TCudBPsjM2p5OBLWfNi9PRowJ6Y3whOCcFMYcxft4/yN6fpObBXnl4HIE8rXEIIDyHEX0KIG0KIa0KIDMPBQojmQojLQoiLQoizQoiq+nBjIcQZIcQlfdpJadJMSZPmdyHE6914yrxzzh7/lyLWhdOF2RnbEvEsIqMeqjN6nM2JHgYYq9l16wITDm3mQuQdNAaRFLW3ZOu5M5S2c8VIqSJJr4fmhsZYYZCpHtatX58AQ8GeW1eISkzgUUQYs/89SqvuXXOth48CblPAwSJdmBACH1uTXOqhKVcfPaei+8vfCmMDBVp1CkXs37Me2jkCcC8shGI2jhgoFAh0o7W2drbU8C7wej00zEwPbTLVwzb12rD1m83cWXCL4+uPsnX1FuoqK6BOlujn2gUrpRUlTIripyhNU5N2HGc34ZogjLUmnLz/F5ct/qb3lz3z9DoAsh7KyMh8cnw3exYLF8zCw9OG23e/44uOBWnRognnzp1LF8/IyIjdu/fx1ZBtVCw/hQb15lCl4lSmz/gub/dkziWfRAPUyc2F5/Gh6cKSNSkoDA3STesqVKgQjxQZKyIPpCcUKFAgy/wNDQ1ZvGE1t23UFHFR0alUPpr7FWR8tQoUsjMjIOoZCeoUjE11e7tpJS3R6ngsLCwIDw8nOjo6XV5LN67As0sJtokjnHC8Td+5X9G5e5dcn3eh4iU5/SiYiLiXPfJarcTtsFg8PT1zlEf+/Pm5EhSNvaUpT6LjU8MT1FqMTUx4EpOAg4NDtnm8eo5vil/Jklx59hwAOzMznsbGoZUkJEHq9LKncXEYm5sTFRVFcHBwhlGVwkWLcishLEPet+JCyJ8/f7qwySPG0tqsLG28q1PT258vCzTF7GkKgdHhOBta8CAujHL2npwNfUpZy4L4WXjQwbM655PPsi5iC0c1J/lh/U+4uLi89bm/Y9TAMEmSCgMVgYFCiFc3WjwMlJQkqRTQA1ihD08CakmSVBIoBTQQQlTUH5stSVIJfZp9wPh3ehYyOcLZzZnghJB0YSmaFBRGiox6aBCYIf0Dnr5WDxetX8N1Sy1+9iZ0KFqA5n4FGFu5IgWsLbkZFkRCSgomafQwIjkxSz1cuHY1Fg1q8MOTm+xQR9Fp0lg6duua6/MuWKIkpx6GEh730vO3VitxKyQuV3p4OTgGe0szHqfRw/gXehj9HvWwVAmuBOnuj62pGYHxMal6+OI+PouLfa0e3k4MzZD3rfiMejhh2ATqJNekoX0DKjtVopN9RwwCFITEhWArbAhMDqKIkR83EgPwU5TEW+VLPdOm3FKc4QBruWTyN4t+/kHWQ1kPZWRk3pLExES+n/0d23YMwsLcGDs7C7p0rc7Y8c2YNWtahvhly5bl3r2HzJq5iMFfTuLhw8f06P4OOgNzwSfhhKjrwO5M6z2ebsatMFOZotaq2fPsD1r2/DxdvGLFimHqZ8kfN49R1b48EhJHQk7i7O+RbYULdPu3JUaH07tODeKiIokJDcdQqWRA+WIM3HMMezNbSth6oJW0/ProNH7lStDniw4YxMSRpFHj4JuP8bNmYmtri6mpKT369KRHnzd/OMLCwvhj314u/3uDM1fuYGRsSM8aZTh87zmfNWuFubl5jvJxcnKiYIXqPLn6D5duP6Gkkw2WRioSJQUXHoVgn78wjo6Omaa9e/cuM8YORxEfTopGi5WbL2Onzcb+Dffx69C9O4M6dsREqcTf3Y0YjZadtwJoWFrnUfHCvXssP3oC7xu3+GnCOKyt7LBxdmHQmJHUqFUL0L2Eyx1N+fXeBep4FkOt1bD3/kU8yxVPVwmNi4sj7P5z8heqkhomhKCGY3FOPrpEYnIi/8QEEJEYj5FCydKnu2nlUIM7Cc8JTIjE1tiWJzEhnD11Fh8fnzc63+wQeTiVTJKkZ8Az/fcYIcQNwA24niZObJokZuh3fpd0Q2Yvjqn0nxfHojNLI/Nh6TaoO5N7TqSDUVtM9Xq4P/Qgrftm1EMzP0sO3zxKFbsKgMTfoSdw8XfPkR4mREbQo2YN4qKjiAkLx0ipol/pEgw9dBQnCytKenmilbT8cusiBcuUom/HL1DGx5CkVmPv48u4Gd+l6mG33r3o1vvNZxKEhYVxeN8eLp+9wZlrtzEyMqJnzdIcDgjms6atc6WHhcpX48nVE1y5E0gpZxssjZQkSoILD0Kx881eD6d/OxyDuAiSNVqs3PMxbvqb62HH7t0Z+IVOD0u7eRAnadhz7yb1S5QGAZfu32X1qaN43L7B/DETsba2w9bVmS/HDqdmWj10NmX/vfPU9iyOWqth38PzeJcvlkEPgwOC8LJvkhomhKC8WTkuBf1LoiaJ83FXiUyORSUM2aVZT02pMQ+lhzxXh2Ah7IiKecaZk7IevppGRkZGJjO0Wi1nzpwhJiaGSpUqpfudCgwMxMzMCF9fJwLTTFapUbMwixcuzzQ/pVJJLb32fwx8Eg3Q0qVL02/aYBbNWoBBAiSKZJp3aUXXXt3SxRNCMG/ZD6xftZ61O3aiUCho3LcJHbp0zFE5Qgg0khYbOzuUKhVhoaEoVBLCRnDA/B6HAu4Sr02mSv0aXPrrMCNKlMXdWues4/zTxwwfMJAVP296673kJElixMD+tHO0YGyvDoSFhHAn8Bkjtv/N6NlzaNX689dnkoYR4yaxY9tWrqxeTu8DF3G0ssDU0ppSVaozYczYTNMkJCQwZlAvJtbxwMfRDYCz94IYOagPK37+5Y3O0dHRkflr17Jq4ULWnziFVeGiXBVw/MxFkuPjeR4cxOSalchvaoKlkRFLL1zDQmXGsknTcXV3p2BB3TqnH1cuZf2qNczcux+lSkXjTq1o26F9urKEEEiZ1A/MLS14FhpDXEIsLZ1L4WfhhI2hKU/joph1/QDFDUvR1647GgNIMlGzbfYW3LzcqFy5cq7PN0skQJur62cvhDib5u9lkiQtyyyiEMIbKA2czuRYS2AG4Ag0ThNuAJwD8gMLJUk6nebYNKALEAXUzI3RMu+G0qVLM2DGIBbNXIiIEiQpkmjRoyVde6UfVUyrh+t37EChUNCobxM65lQPFQKNJGFjq9PDkNBQJAOBxkzFFk0oO88fJk6TQqU6Nbn992HGVSyGh61uOujZR4GMGNif5Zs254kejvyyHx29DJkyuCWhoSHcfhLE8K3HGDVzXu71cLxOD6+uXk7PfZdwsrbAxNKaUpU/y1YPRw/sxbjqPvg4eANw7sFzRgzsw8rNb66HP6xfy8oFC/n532NYFSvMbQGnr57W6WFQEKPK1cJbaYGFoQlrbp3DUmvDknGzcEunh0tYv2oN3+05gEqlpHGXnOuhhZUFIaERxCbFUlVZAy8jLywMLAhOCWVD1M+4SWWoLQagFVq05imsnLodD29ZD5H1UEZGJhuuX79O69bNUShSsLO35OqVR3w/Z07qqKWzszPR0fE8fRqOrn9Mx79n7r62g/hj4ZNogALUqV+X2vXqEB8fj4mJCQqFgujoaNYsXcGJP49gYWVFux5dqFOvLj379aRnv9yPPtZq0ozdx36nfamiWFhaYmFpyb5rd+jWrz9Dho8gPj4eIyMj9u7eg/GVa6mNTwB/Nw8On33MnTt3KFiw4Fud682bN7FOiMHfsxAAjs7OODo7001hhFKpylFlJzAwkNWLl3D13DmcXV3p2K8vO347DEB8fDwqlQqVSpVl+sN/HKKmpzE+ji/XF5XN58T+mze5fv16ptsu5ARXV1fGTks/vSA5OZlB3bowuXZFEoOCsDY2BgTdShTmmz9P071CLbauXcfYaVMB3TYHvQf0o/eAflmWY2pqinMBT26GPsTPXufsRJIkziTdx7tSKW78dZJCVs5otRKhSXEYq1QoJANKGJUgSiRgbGyMh7snDRNNWb9oXd5WuCC3zjRCJUkq+7pIQghz4Bdg6Cu99QBIkrQT2CmEqA5MAerowzVAKSGEtf54MUmSruqPfQt8K4QYDQwCJuTKcpl3Qt36damTiR6uXrqCE4ePYmFlSfuend9KD2s3bcrevw7TtngxLCwssbCw5Nebt+jW/xU93LMbizsXUxufAGU9XTn44Hye6aFNciTlfHRrXZycnHFycqanxjRXerhmyWKunDuHs6sbHfvmXg9ruJni42CdGlbG25nf7lx9az0cNz2jHg7s3I1R5eoQHxiMlZFuqnOH/CUZf/4vOpeuz5bV6xk3YwqQcz10K+xOwN0A8lvqpuZKksQ11XUK1SjC+f0X8DL0QtJKRGqjMDIwRJIMcNOWJt44FmMzIzzcPLBMacyqBetlPZT1UEZGJgs0Gg3NmjVm1Ji6dOteHSEEN28GUqfmSEqVLI2/vz+mpqb06duXTh2WMmbMOCRJ4vffrzB2zA42b/7lQ59Cjvgk1oC+QAiBmZkZCoWChIQE+nXoiur4Lcb41qCLeSF2TP+J1UszH7rOCZ279+CBlROTjpxh+6UbzDh2lrMY0/fLwallK5VKQoODcDTO6EDD0ciE0NCM63FyS1hYGM6mxhnCnUyMCAl6vbONoKAghnTpSpGnz/iuXEXamFmyYMRI/vzjD0BXGcmusgUQFhKCi0XGOC4Wyjw5x7QYGhoSExWFvZkpCqFzyg9golSCJOFkbkno86Bc5zv+uyn8qrnG+vt/cuDhGebc3olvgzKMnTKR0oWL453fF59CBfApVADvgvkxMzLDysYG7wLeuHt5oFAosDW2ISQo5PWF5RYpF58cIIRQoatsbZQkaUe2RUvSUcBXCGH/SngkcARokEmyTUDrnFkj8z54VQ/7fNEN6dBDvnJqSBtRiq2TFrNq6YrXZ5QFnbv34KGtPVP/OcmOq9eYdeoM55WGGfUwKBhnU6MM6Z1MjfJMD13MDTOEu5gbEppDPRzatStFnz1lTsVytLc0ZeHIEbnWQ2ezjDY4m6veiR5GR0VhZ/JCD3UYK1VIkhYHUytC3kAPJ30/iRMWp9gVtocjIX+zKnQNRVsUZ9zUCRQvWAKfQvnIV9gXXz9ffAv7Ympkjo2dDd6FvPHQ66GFyo7g53l7voCshzIyMv83HD16FCsrFd17fJbaQern50q//jUYO3Z06lr+aVNnUKdOG27dfI6ZcQ9GDd/L0qWr+Oyzzz6k+TnmkxkBfZX9+36lmNaSmr66nmcHM0sGFq/FuI1bad+lU649LILuh//7hYu5ffs2AQEBVPf2pkiRIkiSxNmzZzl97BjPgoNJTkzg7u1rVPbOh4HeeY5Gq+VSRCiDixV763MrVqwYC4Mj6KLVpuYPcOJ5ON0rVckmpY4Nq1bRxs2T8p7eAHjZ2DKyfGWmzJtPzdq1czRiULZCRVYd2ED9ki/DtFqJ049j6JhLr1sRERH8tn8/0RGRVP6sOsWKFctgg3+lypx5dAc3IdDoz/teRCS25pacevqQci3r56pM0E1x27h3G+fPnyc4OJiepUrh6uqKWq0mSBuPpDTARPWyUqlRaUgySUGpfFkZvRJ+jUrNKmXIW5IkLl++zMm/j2Ntb0vDxo1ybpgESHm3n53QXcyVwA1JkuZmESc/cFeSJEkI4Q8YAmFCCAcgRZKkSCGECbpRgFn6NAUkSXqxl0Mz4GaeGS2Tp+zft5/CibZU8yoBgL2pFb0KNGT6um180aXjG+vhnEUv9bDGq3p4/BjPgoJISUrk0NU7VM3vlU4PLwRH0D+P9HBRYAw9X9HDY48j6dLj9Xq4cdUq2nm6U8FbNxPCy9aW0ZUrMHH+vFzp4cp9m6iXRvq0WokzT6Pp/KZ6GB5J5RqZ62GZypU4e+MRzoqXevggOhw7U2vOBt+lwhc1clUm6PRw8/4tqXr4dRo9jFJFIVQCI4M0HQmGaiTzJFTKl9WM+3GXqdq6Yoa8X+jhiSP/YONgS8PGDXNumKyHMjIy/0dERETg4mqTIdzVzYYzZw7i6GjL3r0HqFSpEuPHTeDIkSNERUVjbJxx4Olj5pNtgF4/f5GStum98RkoFHiaWPPkSfZeb19HwYIFU6eNabVaRg8dQnLALQqgwSQhntNPnuFsbUHHjcsZX7cZQsD2u7do2rlTphug5xZra2vqt+/I5B1baFs4H8YqFQcCHmBWqEiOXC7fuHCRJr6F0oVZGhtDQgIpKSkYGmbsyX+VIkWKYO7rz+yDF2lRwoXEFA0bzz2ldstO2Nra5vhczp09y/RvRlDbwQ1rQ2OW7NmPS3l/vp0yOV2lq1vffgzq3IkaVsa4RccSFpfIttsPKelViPMksuTzN+tsVigUlC2bfraWUqmkz/ChfD9jPs1dSmBrYs7J5wG4+/tyMPhP4sMScDF1JiD2LldNb7Kq3+p06bVaLRNHfUvQmRuUt/TkiTqBbotX5cIqkacVLqAK0Bm4IoS4qA8bA3gCSJK0BF1vfRchRAqQALTTV75cgLX6dU8KYKskSfv0ecwUQhRCN0HuIZD1HD+ZD8rVsxfxs0zvCdZAocDdyPad6GHKvVsUECmYJiRw5PEzXGzMaL10I1Ob1UMIwc/X79GoY5e808O2nRi7ZxMdS3lgrFKy9/pTjPOVyJkeXrxAs0LpPcJaGhsjEhJzpYcWBUoz5/AlmhVzJylFzc8XH1Ondcdc6+G0r0dS09YTK0MjFu3+DbeKpfl2yqR0eti9f18GdOxCJaUVTrHRRCQksOfRbYp7+nHVOJov2+StHg4cM5Al45dQ1bAqViorLsdfpmBFb24G7Ucd+xl2hq4EJt0m1OkKs/uvTJdeq9UyfsQ4Hh+7S3HDgjzTBrDhp7W5sErWQxkZmf8fqlWrRs+e3Xj6NBw3N93vg1arZdPGE3z3fQeePg2nWbNGhIS83L/6v9b4hE+4AeqZPx8Prp2koN3LrbgkSSIwIQonJ6c8K+fgb79h/uQBTX3dMdeqMTNSUc/Xk/kXrtGpQhF+unOZsuXK0++7Gfj7+2dIHxsby5MnT3BxccHKKuN+bVnRrXdvivv7s2/rZhITE6nVdwi169TJUW+9Rz4f7oaFUtL15cLmRHUKyQrFa6eavUAIwYTps/nzz8NsObAblcqIjqO/xtfXl5s3b+Ll5fXaURWtVsvMMWMZ518VW1MzAKp4+zL/7D+cOXOGChUqpMZ1cHBg2Zat/LJlCweOHSU0RoN3xSqUbFCX8S1bvtEITnY0aNwQb18fftnwM+dCQ6nctyWjmzYhNDSUzes2c+HONUpWKMmI9t9mqEQfP36cqH/v8GWRuqlh5eJ92XX8jxyXn8deH4/zYt5y1nFmoe/JfyX8MjonHZmlkaeY/UfwLpCPxxfOkd/m5TsvSRLPkiLyXA8tAh/QLL8r5lIKZkaG1M/vwbxz1+lUqQizL96hXPny9J42O0/1sGuvPhQrXYZ9238mKTqRmj2+oXbtnOmhu08+AkJDKeWWRg9TUkhWiNzp4Yzv+PPPw2zfvweVoRGdxg7OvR6OHseo4p9ha6rzhljJswALTh/NVA9XbNvML5u3cuzoUULCk8lXrTzlGtaleau818OGTRrik9+HrWu38jTkKbUb1KZxs8aEhoayac0W7t0+y2eVStDmi28y1cOgY4/o4tYqNaxEUmF+5bccly/roYyMzP8LDg4OjB7zLZ9Vnc6Qr+rg4GDJmtVH0Wq1tP+iEkqlAT/OP8ju3btp3rz5hzb3jflkG6DNW7ei58Yt+ITZU8DOhWSNmt13L1CqRpU86XV/wZH9v9I6vxeJYSE4WeoqDe6W5igliZp+BTgWe5uJ332XIZ0kSSz5aT5/79tBQXtz7obFUbp6Hb4ePTZ1z8vXUaZMGcqUKZNrmzv17s23PXsxzNwcV0sr4pOTWXrhLK27dsmVt0aFQkGdOnWpU6cuycnJTB8/jrvnz+BhYU5AZAzNOnelU7fuWaZ/8OABLkrD1MYn6CpytV29+Wv/gXQVLgArKyt69OlDjz59cn3Ob4Kfnx/fTp2ULszV1ZWvR32dbbq/fj1Idcf0jlXsTC1yV3je9vjLfOI0/7wlPTZsxTPCEV8bN1I0an59chr/WpXzWA/38Xl+TxLDQ3Cy1r3X7pbmqCSJWkV8ORp5jwkzZ2dIJ0kSSxfM5+iB7RRyMiEgOIGSVevx9chx70cPe/VihLk5rlY6PVxy9gKtunR9ez08+y/uFubcjYqhWZcur9VDJ4VRauMT9FtDOefjz1+z0MO+venRt3euz/lN8PPzY/yM9Ftburq68s2Yr7JNd3jPIcqbl0gXZmOU884FQNZDGRmZ/ytGDB9J2TLlaNu2JX6FnencpRqdOlfByEjX6enkZMXjx49fk8vHzSfbALW2tmbu6mXMnTKDVef/RSgNqNeyKb0H9s/TcoxNTYlPjsYYgcTLbtVEjQaNVoOhUUbnGwB7du0k6J9fWda6LAqFQJIklv1zkvWrV9K157utUPj6+jJq3lwWzvqOyPP/YmBizOfdutG6bds3znPRvLk4Bd5lUH3dmiuNVsu0bRvx9MlH9SwWTBsbG5OgTskQHp+SjLGZ6Rvb8qExMTMlISX8zTPIhTMNGZmcYG1tzby1S5gzaQYbbx5HoTKgfusm9B6Yt7METUzNiE+OwkSAhITQK2KCWoNaq81aD3fvIPjsLlZ3L5Kqh4v/+Jv1q13o2vPddjj5+voyau5cFn03i4h/L2BgnBd6OA/Hx/cZUKcaoNPDGVs2vbEempiZZZLiv4GJuSkJ6sQ3z0DWQxkZmf8gERER/PLLL8TExNCgQQMKFy6c7nitWrWoULEqSmUQPXvVSO3wvHkzkHv3gmnfvn1m2f5n+GQboABeXl78sGLJOy2jefsvWDZ6BP0L+xCZEI+NqTGnnz7HwcqCnTfu07Rd10zT7f55HZMqF0ShgMjICCLDwqhmK/HV3FnUrt8Qd3f31LjR0dEs+2k+Z4//jYFSSa3GzenSs3eOp4dlRqnSpVm++ec3Tp8WSZI4dvAAixtWTQ0zUCjoXqowa9avzbLC5erqimRrzY2gZxR20q3XTVKr2fvkLhMnjsgT2z4Ezdq2ZvLBoRR18ERloHsFb4TmsicrD6ecyciATg9/XPVu9bBZ+y9YPmY4A4p6Exmv18MnQTjaWLDzygOatM18BHDP5rXMaOj9Ug/DQ6nhnsLAH2ZSu36jjHq4YD7nThzBwEBJzUYt6NLj7fVw2c+b3zh9WiRJ4uhv+1lU96XuGSgUdC1RhHXrstdD7K24GRKIn4Nu6UiSWs3+Z3eYPC37WRcfMy3at+TbfSMoaOOLSqHTwzuR93OXiayHMjIy/yF+//13OnRoR63aRbGzM2PWrKl07daDmTO+SzezZumSpZQoUYQWzebStVs1Hj4MZca03bRp0w57e/tsSvj4+aQboO8Df39/qn7RkWlr1+CiTuRxeAQRajXObm54FCtHy88z3wQ9IS4OSxNDwkJDSYwKx83CGIWFIVYkM6xXZxZv+gVbW1s0Gg2De3almYuCPs1KkqzRsOXMfiYF3GHq9/Pe89lmjiRJGCChUKSfJmVjakxsdPbbIEz7YT7D+w3A9uk9rFRGXI0Ko8vgQfj6+r5Lk98pfn5+tP6yJ5MXLKOwmSOR6kTirHJZOc7dxusyMh8F/v7+VGnfianr1uCsSeRxWDgRajUu7m64FamQtR7Gx2Fp4khoaChJMeG4WxmiEEqslQl807cTi9bvSNXDIb270NJHy8AvCpGs1rD59B4mj7nNlNnz3+/JZoFOD8lED02IjX6abdrpP87jm34DsX52F2ulIddiQuk6dOB/Xg+/+KYzC+evIp/SnRhtHCmOucxE1kMZGZn/CAkJCXTq9AW/7PqSqlV1Dj8nT21F5QpTqFe3AbVr106N6+7uztWrNxkwoD/Dh20jJUVL7dqNGDFi5IcyP8+QG6DvgU7dutOsVWtu3LiBWq1GqVSSL18+HBwcskxTpko1jty8hI8yAS8bUwSCbRcfgYGEeWIIP879nolTp/P3kSMUMU6iXlHd0L2xQknXCoUYtu8CT548STcykBXR0dHs2bGLezduU9i/OI2bNsXUNO+muCoUCuw9vLgbEo6vw0uPj4fvPKBSmhctM5ycnFi7Yzu3bt0iOjqakcWK5alt7xJJkjh16hSHdv+BubUZLdq2IF++fAC0bt+Ghs0ac/XqVaytrSlQoADrFFtymDHymieZ/yxvoof+lT7jz6unyGcai4+9MULA5lPPwAAsNM/4cd73TJwynb//PkJRi3jql9R5rTU2VNKtWj6Gbs2lHu7cxYObtyhUqsQ70UMHDy/uhYaRz94uNfzPgPtUql0n27ROTk6s27EtVQ9H/wf1cP+uP7CwNufzds1T9fDz9m1o9IoeblDkcAaOrIefNEIID2Ad4IxuLHyZJEk/fFirZGSy5vDhwxQp6p7a+ASwtTWn34AabNn6c7oGKOjX0n8znFatmlO5SkFs7SKpW7cGn7dpxw/zf3rf5ucZcgP0PWFpaZnBSUR29Og3iL4d21KcEGrnd2T1v3cJ00QzvLkHkgRLjm5i1TI3JElBEfuM638K25ny4MGD11a4AgMD+bJTLyoZeVDU3JFblw7RbfVGlmxak6vtAV7HsPETGdWvN4097PGytuT8s1Cuqg1Y0KnLa9MKIfDz88szW94HkiQxdthYHv4ZSDFlKaI08QzePJT+k/vSuFljQLeBffny5d8sf7nHX+Y/TK71sO8g+nU5QkmTIOoXtWX5sUeEpMTxTSedh95Fv65j1Qo3JI2Cok4Z3dEXcTLJsR4O6dqdGlZOlLW248aWPfRcu4GFG9bmrR5OmMCovn1o6OqAt7UV55+HcFUSLOzc+bVp/6t6OPLrcVz8/SlOlCJZG8++n79i+NQ+NJX1UObtUAPDJEk6L4SwAM4JIQ5JknT9Qxsm82nz4MEDVqxcztOnj6lQoTKdO3XGzMwMrVaLSmWQIb5KZYA6k3X+KSkptG/fhlVre9KwYUkAZsxqQ7XK09mzp26uPMJ/TOTMfeD/Gffu3ePEiROEh7+FI5h3jK2tLSs2/8KhZ2p23okgTBPPxoHFqFrIhuJeZvzQszDH9m/AxsGJqyFxGdJfD43Dx8fnteX8MH02n9uUpJ5XaQrYudHEuyx1VPlYsSBv14L5+vqybOsvKKo15LSFC34derJ048+YvaXzDK1Wy+XLlzlz5gyJiW/hyCJNXqdPn86Ql0aj4fz585w9e5aUlIwC8SoXLlzg3l+PaGbzOfks8lPUugTtLbuxYOoikpKS3spOnSur3HxkZLLmv6KHyzfu5OA9BdsvxRGSnMSmcb5UK2FJcV9jfhzmw7Hf12Hj4MSV5wkZ0l97npAjPfxx5nd0di9I44JF8XN0pqVfCZpYu7By0eI8PR9fX1+WbduOsmZ9zlg7Urhzd5b9n+vhhUMPKWnWBmezAnhalMTfsBvfT5b1UObtkCTpmSRJ5/XfY4AbgFv2qWRk3i1HjhyhXDl/YuMuUamyIb/+uoqKFcsSHh5OrVq1OH/uHhcvPkyNHxeXyLIlR2nVsk2GvE6cOIGzs2Vq4xPA0tKUL4fUZuvWTe/lfN4Fn9QIaExMDCMHDkb7LBRXY3N+iA6hRqtm9Bv8Za7c6b8vrKys6D14KHtW/ETzMnYolQriktREJmrwdLWjll8CBgYG3Ew24uC1h9Qp7EGyWsPP5+7iVLg0bm6v1+A7V67Twa9FurDyrgWZevTXPD8fGxsbunTPepuB3HLv3j1GD/wST4URJgolM2PCGDR2NLXqZD+NLTPu37/PqP5DcFEbY2KgYkZ8MIPGjaBOvbpcuXKFMQNG45LshAIFTwwCGT9vAuUrZN1bf/TQUQpRLF2YoYERLloPrl+/TunSmW4Vl3Nkpxsyb0lMTAzD+w8l5VEETior5iQ8o3abxvQfOuij1cNeA79i37ofaF7NEpVSQVxiChEJWjyd7ahVKggDAwNuxJrx26XH1C3uRrJay6aTD3As5J8jPQy4eo2+FdJPf6rk5cOoo3/n+fm8Cz0c0XcozskWGCtUTE1+xpCJw6ld9830cFjvYdjEWGIsjHggPWLo5K+pW1+nh8P6fotZjCsCQbTJY6b9NJ4K2ejhn4eOYacpni5MpTDCPFHWQ5m8QwjhjW4f1tOZHOsD9AHdPotHjhx5r7ZlR2xsrGxPNvxX7ElJSeHJkydERUUCsGLFj1hZ6ZZIDPu6EQ8fhnLgwAHc3NxYsXI1x/9+yLXL8RgoFUSExzF8+ETMzc0z5B0TE0OvXoM5diT9/s32ttWpXTvlo7s+OeWTaoDOnDCJiknGVCldAwCtpOXHPYc4WrIEn9Wo8SFNy5LO3XvxJDCIG+dX8zAiAWNTEzy83VEqDQiLBy8bG35csZaVixbQd++fKJUq6jRryYBuPXKUv4GhiiRNCsZKw9SwmKQEzMw/brf+Wq2W0QMHM8i7OB7Wuqlx8cnJTJg8nWIlSuDomHMvFlqtllEDhtDdvgweVjqvYvEpScyc9B0F/Qoxut8o2pu0xsbSBoCY5BgmDp7A1sPbMDc3zzRPa3trnkp3M4THE/v20yXkbQdk8oAZ46ZQIsyGCvkrAzo9XPHLbxwtXYLPamTuifVD07l7b50e3l7G/dBEjE1McfdyRqlUEh6jwMvGhh+Wr2Pl4h/p+bNOD2s3+ZzxOdVDlYoktRrjNB5zoxMTMcviPf9Y0Gq1jOj3FZ3Mq+FqoVtLm5CSxLxx31G8ZO718Js+w2isrYuLgzMAiepEfvh2HoUKF+Kr3mMom9ABC6MXuhvDqAGT2Pv3liz10M7emmQpIEN4skLWQ5m8QQhhDvwCDJUkKfrV45IkLQOWARQqVEiq8RHV+Y4cOYJsT9b8F+yJjo6mVKnidOhUhsZNStK6xXwePf0JIV7OyDE685x+vTdz6ZJudvjjx4/5efPPxMTE0KhhIypWrJhp529iYiJeXu7s2P0lFSvq/BskJaVQrfJUBg8eh7m5+Ud1fXLKJ9MATUlJ4ebZ8zT3rcjdW3dAkjBQqWjkWoDdGze/twZoSkoKS3+az98H96GQNLj5+jF09Hg8PT0zjS+E4JsRI+jy+R8ore1wddT9wD94Hsvph4KBlSqhUqn4atQYGDUm27K1Wi1rl69k35btoFZj7+FG2eqV2fX3Gdr5VkEIgVbSsvX+SVp/9fq1SB+S69ev4yFUqY1PAFNDQ+o6enFw/wE6d8t8e5vMuHnzJk4pRqmNTwBTlRE1bfKzZOEivNWe2BjZpB6zMLSgsLYQR44coUmTJpnm2aR5EzYv6krhlKKYqywAuB97F6WbSHW88VbITjdk3oKUlBSunblIDfva3L19B0kroVQpqW1djJ3rt7y3BmhKSgpLF8zn6MG9CLS45yvEkFETXqOHI+na7g+U5ma4uuh6lx8ExnH6tooBk/V6OHIsjBybbdlarZa1K1awf9tWnR66u+NfrQpbTl+kS7EyqXq44foFWg3omefnnpdcv34dp2QzXJ1fOnIyURlRxawwB389QOfuudNDqxgLXOydU8OMlcaUNSjN4gWLsY7xwcIkje4qLXCMKZqtHjZt3pjVC7riqi6GiVKnh0HxAVjnk/VQ5u0RQqjQNT43SpK040PbI/PpsX7Dekr7uzFpcmuCgqJITEwhOVmNkdHLzsyIiLh0nXQeHh6MGP76LQWNjY1ZuXINzZt0pmmzkri727Bxwz9oNDB69AhWrFj9Ts7pXfPJNEC1Wi1R4ZHE20ThYmSFEIIUrZpbQcGEmWdcM/SumD5+DG6R11nVrjhKAwVXH4cyol83lm/ZjYWFRaZpDA0NmTp3BRNHf4mDcSiSJBGaZMbUuStytbfdonk/8PzQSSYXr42RUsmjyDAW/P4HxStXYso/u3AzseFhfBgN2jWnafNmeXXK74SkpCSMFBkXcRsrDUhMiM9VXomJiRgpMr4KxgolcXFxqLQZjxlKKpISs167ZG9vz6SF45k6fBqWsTYkSglYeJsxd9GcXNmWJXKFS+Yt0Gq1RIdFEEc0DiobFEqdHt57HkiYQdh7s2P6hDG4R19lVcciOj18FMbIAd1Y9nP2ejhl9komjB2Eg/kzJCRCYy2Z8t3KXOnh4vnzCT/2N99XrYSRUsnD8HBmHz5EsQqVGXXyDzzMLXkQE0md1i1p0uzj10NDkVGnjISKhPjc/b4lJiZiKDJeR5VQER4XjkKT8ZiB1pDE1+jhd4vHM27YdBTx1qhJxKmgKQuXyHoo83YI3ZDRSuCGJElzP7Q9Mp8m165dofpnBQBwcrKiXPl8zJyxh/ETWiGEIDY2kW9Hb6Fw4UpIkpTrZS5NmjRh2rSZTJo0hvZfVGDZyt7UqFGYbVtPc+/ePbRaLQrFf8utzwdpgAohvgJ6oZs4cwXoDpgCWwBv4AHQVpKkCH380UBPQAMMliTp4OvKCHoWxMAugyherjjtOrXFysqKkKhIopOTsDfWVWxUCiXnQ56SYvZydCs+Pp4lP/zAqb/+BARV69Wjz6BBGBtn9KyYW8LCwnh4+TQj2pZKDSvmYU9Dn2j2791Nuw6dOHPmDPu2/UxyYiIefkW4evoEkSHPsbCxp/fg0Xh4eiGEwMPDI0cPcHBwMD/NmsfF0+d5cvcWi+p2wFCpu+2e1nY0dylEpJ0Nq/Zv4/nz57i7u2dw65+SksLqpcs4vHcvGrWaMlWr0P+rr7C2ttZtqn70KAe27UKSJBq0bkaNmjW5du0aOzZsJCoigqr16tK4aVOOHjnCxmVLiI+OwsXTi77DhlO4cOE3upbW1tYcvHmNJ4+fU9TVhYaFi2NmaMifQY8YW3dUrvIqVqwYAUlhxCYnYG6om2OvlbQcjbzH0IlTGHf6W6prqmJooJumrNFquCpdZ2i1b7LM8/z58+zatBvv/D6UqFSMho0a5mgLiBwje338v+F96OHzZ0H07/wlJcsXe6mH0ZHEWCVia6ibAqlSKLkU+ZAUt5daFx8fz+J5P3Li8BEAqjeoS9/BA/NOD6+cYuQXL9cGFvO0o6FvTDo9/PWXTSQnJeJeoCjXzh4nMlSvhwPHvJEeLvx+Dpf//ZfHd++wplULjAx0euhla0s7H2+e2duwfM+O7PVw2VIO792LVqPGv3IV+n/1dTo9/O2XHUiSRP2WLV7q4caNRIeHU6VevZd6uHQJ8dHROHt50W/YN2+lh3/du8Djh8/xc/SklncZTFVGnIy7xZT6/XKVV7FixXhMIPEp8ZiqdOeulbRcVF9hdM9vGXZiPCnaGqgUej2UNDwzuUy1al9mmef58+fZtnE3PgW8KFu5OI1kPZTJO6oAnYErQoiL+rAxkiTt/3AmyXxqFCzox+lTOxikl8HlK3tTt9Z01q89RqnS3vxz/BZVqhbiyuVTTJ06BQOlAdeuXqJAwcL06d0XV1fX15Zx8OA+ZsxqS6fOVVPD2rStwNJFEZw/f56yZcu+q9N7J7z3BqgQwg0YDBSRJClBCLEVaA8UAQ5LkjRTCDEKGAWMFEIU0R8vCrgCfwghCkqSpMmuHE2kllL3y3HvWgBdtnXlp3U/4uXiyZJbZ6lk74arqSXnwwMJ02gwVxoBOlfxQ3v3pooBzKtWGa0k8dvFc3wzoD8/rVz11o45nj17hrdtxj3b8juac+r+XVYuWczl/VvpWNoLyVDNmk0LEUZGLOtQk6CoeGbN/JZuo6dTpWq1HJUXHx9P/w49aagsSSWXxmwIiiExPJYgtRpnF930Kh8re/bcvY+lpSWWlpaZ5jNx5EjsHz1letlKqAwUnLj/gIFdu7H2l+38+N0c7h86RUO3YggEu6YuYN3ylUhBQbTzLYSNiQVHNmxmzaLFuKk0jKlcClszU+6GhDNpUD+mL1+d6ylYly9fZkL/4XT2qI1RjIbHj4IZcGUT7t6uVG/Tgvz58+cqP0NDQ4ZNGcvM0ZOpbp0PU4WK45H3qdSqAf7+/vT7dgBLpy6htKIEBig4r7nMF4M74uTklGl+G1Zv5Jd5u6mgqoGTgQmnzp3l0ukr/Lh8ft70UEnytgP/L7wvPVSHSxS4Xomb5+/QaUs3Fm74AS8XL9Y9PUYZS2+cDa25EvOICFKwVloDOj0c3KMvpePNmFS4EVpJ4s+jV/n62iAWrlmeJ3roY2eSITy/oxknH9xl1bJFXD30M10quoBWzcqdv6M0MWRt30o8j0hg2ryRdP76u1zp4ZdduvGFqyctSldgQWQEKVFRBKlTcHJxAcDHzoYz9+5lq4eTRo7A8flj5lQrg0phwLH7DxjUrStrtv/Cj99/z5Mjx2merwBCCPbOnseGFSvQBgfTvkABbM3N+WvTJtouXoyLQmJEhTLYmppyLzSMiQP7M2PFqjfSw9E9RtPIvAWKCEHQ3UBG3lmMZz4Xands+kZ6OGrmaGZ8M53SogRGwpDLmqvU+KIW/v7+DB3fl/kTluGeWAYFCh4bnafnsPZZ6uHa1RtZ8f0uXBXVUBmYsO3Mec6dusziFT/Ieijz1kiSdBzZvbHMB6Zrl658P3sW8+YeoG+/WhgaKlFrtHTqXBW/wq7M/K49+fM7c/36UyqWG0/r1hWo16AIp0+dwt9/Eb//fpgSJUpkW0ZKSgrGxulnoAghUCgEycnJ7/L03gkfagquEjARQqSg6+kPBEYDNfTH1wJHgJFAc2CzJElJwH0hRABQHjiZXQEmBqbYGtlha2SHcYQJG1duxMjKnK+L1Odq0AOeJkRTtUB5opMTSCync89/4cIFbGOjqV9e14tgADQt7Mf1U2e4dOkS0dHRxMbGUr58eezt7TOUGRwczNmzZ7GwsKBixYoZpoN5e3tzIzgOrVZCoRCo1Rri4uL4+0YgHo0a8tuGlSxoXob4+HiCg8IZX7sos4/d5NqTUIp5ODCmThGmLZif4wrXr3v2UULjTnHXAmi0WgLjo9FqJKIiorB3dEBpYMDlkKcUa1wlyzyePn3KsytX6V/55Zqwqj75uHM5iu3btnHut7/4tkTj1Mpod4tq9Ng9jw0dumCpHznoZG3D3b07qFTKG1szXZivgy09i+Vjw/KljJ8xK0fn84J5k2bRy602lgpjEq0S8UrxwDjUgpQqbvQfMjhXeaWeU/XqFN6zhYMHDpAQG8+EOl+lVtyatmhK2QplOfTbIdRqNT0b9MPDwyPTfGJjY9mwYBMdrftjIHRThGubNGH/ue38+++/udr7MFtkpxv/T7xzPTQ2MMXG0A4bQzsMI0zYsGITxtam9PFoyfWwuzxPjKJCvsrEqONQltc1JC5cuIBFaCK1CuueWQOgvk9J7tz4M8/08Prz+FQ91Gj0enj9Ge71m3Bw8zKWdS5CQnw8wUGhTG6RjxkHHnDlQQQlfGwZ39qbCUvm5lwP9+6lsoUVZd090Wi1PImJRdJKREdGYe/giIHSgIvPgihao1aWeTx9+pTn168wpGbl1LDPfL25E3mZ7du2cen3w0ytUiNVD/tZWdF23XI2de6cqoedS5Xi7q5dVCpcAFt9WD57O7oXLsCGZcsYP3Nmjs7nBbPHf08Lk9aYGpuRaJyIe4onJnHmWNYwZ8DQQbnK6wXVPqtGkd82cPDX34iPi6dD3a6peti8ZVPKVyzL7wcOodZoqNege7Z6uPzHjRQz641Cr4eWRg25+e9OWQ9lZGT+b7CxseHPP/9m2LAhjBnVF5VKhaGhAT1718TDwy41XpEibiiVCr6b0x4HB0s6da5KseJuDB8+lIMH/8y2jKbNWrFk8UJatCyLUqnT0+PHb5GSYke5cuXe6fm9C957A1SSpKdCiO+BR0AC8LskSb8LIZwkSXqmj/NMCPHCbZ8bcCpNFk/IYo+ntG627Y1fOmMoYl2MjcdXM2j0EFZMnksHrwpUtSjKhef3+D3uLkt7TAR0buwLmGUcobTSahnRtye187lhZWjAhrmzaNKlBx26vHTssGb5cg5t2UgVZ1siUzQsmJ7AjIVL0vVmm5ubU6dle6Yd2E7borZoEyI58yCcQ1eC0d79npru1jy8F4CZygATSU1QVBKlna24+DCIYh4OOFiaEh+V87367ly9hY+ZC2qtmsXntmIgCX68doRWXsXg7l2CDLUcTQhiRbuM+w694OHDhxSwtM4QXtDCkuNHj1LazDXdSMizmAiK2zigSU4GfeVKAio6unDnWRhpdyYp4uLIprO3c3w+oBuVCX38jAQTb5JSolFiQBIp+Jm5s/PqjVzl9Sp2dnZ06NQp02MuLi506d7ltXncunULN7xTG58vyIcfJ4+cyqMKl5DXPP2f8L700M7wpRfUQhbF2X1sOYO//ZJlY3+kpX0NnF1KcSXsNselq6zoORzQ6aGPYUYPpeYpgm969eYzDy8sDZSs/W42zXt0p0OXl+/HmuXLObR5I5Wd7IhSq1kwLZEZixZn1MMWXzB13zbalbRGio/g9IMIDl0JQRMwm7q+Zjy8F4CFocCEZILCtZTxMONCQCglfGxxtDYhPvppjq/1vRs3KGltR4pGw6zDv6EEZp46TXu/gkh37/JUoeBQWATL27bNMo+HDx9SyDrjyGghKwv+OnaUsrb26fTwaVQUJe3sMuhhJWcnbgeHUL+oX2rcwk5ObNZ7R8wpkiQR9CCI6KhYolPiMNCqUCuS8DLNx+krx3OV16vY2dnRoUvHTI+5uLjQtUfO9NBM65na+HyBpaYgx46clPVQRkbm/wK1Wo2Liwt79uxHq9UihKBevZr8c/wW7b942WF5/fpTtFotISHRODjofku6dqvG4EHrUKvVKJVZN8u6de3G7l2/UKn8FD5v68+Tx5Fs3XKGVavW5sr/wcfCe1+xKoSwQdeL74NuCpmZECLzWr8+SSZhmfZ3SpK0TJKkspIklbVUvaw4RSSFY+9gR516dfn6x2kcMgli5sPDBJWyZ/Hmtam99/nz5+dGTGy6PLVaLb9evcKM2v70qViMdv6F+aFRJf7YuJqAAJ1b+evXr3Pil83MrV+ZtqWL0Kd8cUb6F2TC10ORpPSm9ug7gBItetNnwwnmHL2PxsKMX0bV59tG3ly68wB3CxMczIyxNTXG3dKUm0GRKPXTlJ5FxmJuk3GkISsKlSzC3bin/H7vJN6Gpswo1YZmbhXY/egWMy/8xb64pyz5eX2WU80AfHx8uKXf0ygtN6Ii8StalBBNeoc/lsYmPIuPTfcSCSA4MQEzY8N0ca8GBuHrl/2aJ61WS3R0NFqtbqM3IQSBwUGokpXYqKywUJljr7IhNCKChKS323g9L7CzsyNKt1QvHdFSBE5umU9ReyMkkfOPzEfL+9JD8zR6GJkchr2jTg9HLZnESed7LIraSVw1E5ZtW51ODwOS0j/LWknLoYCzTChfhe4lStO6aHFmVavJwTXr0uvh9s3MrVuVdqWK0qdsSUaWKpS1HjbrQ581p/j+6AO01qbsnFyTca1duXjrIR7WRjhYGGNnboSHjTE3AmNS9TAwLB5zawdySoFixbgeEcauKxfxNTFiUd26tC9clO137jHxn1NsDw5l8caNr9XDGxEZdnjgWkQ0fkWKEpyc3hGPlbEJgXHxGfUwIREzo/R6eO3Zc/K/gR4+D3mOQZIKK4Ut5koLrBX2hEWFk5D4cehhsojMEJ5CJK6yHsrIyPzHSUlJYdToETg62uPs7EixYn7s378fhULBqFHj+ObrLezbd4GUFDVnztyl4xcLqFu/BC2aziUlRQ3ovOOamBhhYJDRsWZaDA0N2bNnP5MnzyM0xBV398+4cOFytr9ZHzMfwmVSHeC+JEkhkiSlADuAykCQEMIFQP9/sD7+EyDt/B53dFPUXoOuopOsSeb3mP10HaQbrfT392feisVsPrCbURPHptsfrWTJksTb2bP7+g2S1WoSU1JYcuoMBZ1syOf0sqKjNFDQPL8rB/ftAeDgnt20KOCBQZr1LJ621thok3n06FE6qxISEtixdT1dKtoxoZkHdQsYoUSDqZEBz2MSOfdE54FSqVRyLyyWEw/CUGslHoVFM/3QDXoOGfb6U9fTqEkjrhsG8deD09RzLopW0uJmas2XZZuwsOkgSFRja2ubbR4uLi54l/Vn/eULJKQko9Zq+CPgNveUgj59+/JQmcCt0JejEOEJsYQpJP4NepZa2QyJjeVIbCRXI2N5FhWDJElcfxbMymsP6NIncwcZkiTx87r1fF67Pl+2aE+bOg3ZsXUbkiQhCQMOhVxCK+kqYYmaZPYHXyRF/eF3I/f29saqgBk3o6+khoUlhnDL+DKNmzXKm0IkdE43cvp5DUKIVUKIYCHE1Wzi1BBCXBRCXBNC/J0m3FoIsV0IcVMIcUMIUUkfvkUf/6IQ4kEa5xAy6XmvepiiTeZo0j66f6kbvfL39+fHVQvZ9vtORk/6NoMeprha8tv9SyRr1CSpU1h95QgF7O3wcUijhwoDGrl7cnDvPkCvh76eGfVQk5K5Hm5fR7calkxq70z9ogZ6PVTyLCqZsw8idWWoVAQEx3PsThRqrZaHwbFM+uUBPQYOf/2p62nYuDH/JsSw//plmvr6otFKeFpZMqJmTdZ16YIiOSVHeujlX5bV5y4Tn5yCWqPl4K0AAlDSp29f7kop3Ah6nho/LD6OEAFnnj9Pp4d/RkdzLTqW59E6PbzxPIjVt+7SpW/fTMuVJIlNazfQqkZDBjTpSOtajdmxRaeHKBScSjyZqodJ2iT+STyOWpvtsuD3gre3N24FzXkeey01LDYphFjzqzRp1jhvCpH1UEZG5gPx9bAhnD9/iP4Da+DoaM6dO3fp2rUDCxcupE6dOtSp25AvB6zBzLg7nTsspG+/2mzdNhhnF2sOHryCWq1hxDeb6dS5U478KhgYGNCkSRPmzpnHmNFjslz+8F/gQ6wBfQRUFEKYoptyVhs4C8QBXYGZ+v936+PvATYJIeaiGyEoAJx5XSGhmhA2R68nXhlHz7E9qVb99euEhBDMXbKU1cuWMeLAAYQQFC5bDtcrGeMqhAB9hUKr1WY6LCEgQ4//xG+H4mHyAFdbI7ydDElWa3ny5AEajRWl3O3YdSuUdRcfozIQaCQFVfN7sOFiINeEE4Om/UCZMmVeex4vMDExYenPq2hcuSZByVGYGBpi7WiDja1NamUlJ4ybNo1N69Yx8ZcdqFNSqFirJgsHDEClUjFv5RImjxjD5ksXEICFmyNbDu5n7ZIl/Hr8MGZKQ5JNjJi1cgUx0dH8tHQRkaHX8S7ox4xlK7Pc72/f7j2cWLuNqSXrYmigJEmdwo+L12BpbYWTnTOKJFum3f4Fa5Upkep4KrtV4bFNaI7P6V0yb/kcxn8zkQ1nj6MShhg7qpg37/s866WSSH308oo1wAJgXWYHhRDWwCKggSRJj9JMBwX4AfhNkqTPhRCG6NYwIklSuzTp5wBReWrx/w/vRQ8jpBD2JK0m0TCO3hN75FgP5y1fzKoly5i6/yAKoaBIdX9cz6dkiKsQAknf4Hkx/ShDfmSih+OG4m55Hxd7Jd4uKpJTtDx5+gCNxobS3tb8cimSVSefp+rhZ34urDkezqU4LwaMX5hrPVy4fh3NqlfnaVwcJoYqrG1tsbG1RavV5ngZ4dipOj38dscvpKSkUKlWLRb01+nhnGXLmDJqNGuPXwchMHd2ZsuBA6xdsoS9R45grlKRZGTEzBU6PVy0ZDERoaH4FCrEzOUrstHDvRxdsZNvCzRHpdfDZT9uwNLGGkc7JwwNLFgZsRRzYUEssZRxqkaU9ZMcX5t3yaIVcxk1bAKXz57AABXWzoYsnT9H1kMZGZn/NFFRUaxft4Eevapy+I9rbNsxlEKFXNi+7TRfDhzG1GmTiI2JZdKU1gweUj/d76KrqzUTx2+nV/dl+PkVZdmBPNqS6j/Eh1gDeloIsR04D6iBC8AywBzYKoToia5S1kYf/5reM+R1ffyBr/P4CJDfLz9LDizC2tr6tcPaaTE2Nqb/4MH0H6xzZqPVaunQtBHPo2JxttJtIKvRatkdEMiIwRMAqNe0GQtHDKWMpxsKhe4BexoZTShKvLy8UvM+deoUZ/75FVdbiXlX4ynmZo63gwk25oLEODWnn0bxS//WGKqUaLQSJiolX/36L9v278VF76Uxt9ja2tJjyADu7j5NU9+X643+eXqLcp9VzSblSwwMDOjcvTudu3fPcMzFxYXF61cTHa2blvaiUjHl+++Jj48nMTERGxub1BevWvXqry0vOjqa78ZPwiEJ5ob/SoPCpSjl4kUPv/IsW76KEpVK4nzJkoY+dUlQJ2CuMmP/8z9p3DbzTdDziujoaDauWc+JP45hY2fDF326UKlSpQzxLC0tmb9sLvHx8SQlJWFj83KbH61Wy97de9i7eRcatZoGrRrTul2bbOf9Z07eTSWTJOmoEMI7mygdgB2SJD3Sxw8GEEJYAtWBbvrwZCCdKzb9Hm1tgaw9u3zCvD899GXF7wvfSA8HDB3MgKEv9fCLRo0JionBSb9Pp0ar5cCTR4z6diSg18PhX1HGI60eRhEqDDLo4elT+3B10DLnUjzFPE3wcTLBxkJLgpTCyfux7B3VGCOVEo1Wi4mhkoGbrrJ17/a30sNuAwdy7a+/aJP/5XTXv+7do1y1nDkzep0eLlq7Js/1cObYSdjGG7AgfAd18peluGM+OvpUY/3S1ZSqUhKjo4585tKQRE0CpkpzjkUdoFn7d6+H61dt4MjBf7Czt6HrgI5Z6uGi5fOy1MM9u/awa+MeNGoNjds25PN2n8t6KCMj89Hz9OlTHB2tWLPqKBevzMDNTTeDZt/eC3xWozCzZn/BsaM3WbP6KF8Orpeq+3Fxifz153UkSUGTps0ZPepbunTpwKFDh7G0tKBb9x6MHzcBIyOjD3l6b4Rarc5x3A/iBVeSpAnAhFeCk9D1/mcWfxowLTdlCCGws7N7fcTXoFAoGD97LuOHDqKSgwWWKgV/B0ZQu00HChYsCEDx4sUp07QVw3dup5qzLREpav6NiGPqT4tSH7hLly4xZ3ofRvQ0pFopc/4+p6LLogu0K+kOCHZfT2HopJmMWL+K6m5WmBjAn48jadNn0BtXtl7QtWd3hp09z8IbhyloZMfDlCiibJX8ODx33hazI7PebFNT0wx76L2O+Ph4+n7RmRqGjjTNX4SwxDg2nD9JaMFoaucvRlR4JLOXLmJgl/7cDrmPo8aGe4onOJVxo3Xbz/PqdDK1q3f77pRO8KCrQy0iwqNZ+NUsngzpQJsvMndaktn5Txs7mZC/79LSuRwGCgP+WvoHJ4+eYN6SH3Nn0PudbVwQUAkhjgAWwA+SJK0D8gEhwGohREngHDBEkqS4NGmrAUGSJN15rxb/h/iv6eGEuXMY9+VgylvZYGGg5J/QYOp2aJ9eD5u15Jud26nmZEdkioYzETFMW5BeD2fP6c1XI1VUrmDKsX9UdJx2gy9KO4OAXRckhoz/jq9+XknNfCYYGwgOBcTyec/Bb62HXXr2ZPj583x36hRFLS0JiIsj1NSU+cNyvrzhdeSlHvZp34Wqwo0GvsUJT45j65WjhPlG8ZlXKaKeRjBn+QL6dRzIo8DbWCU58tz4Ph7VnPi8Xeu8Op1M7erSuhcOj4tQzrQtMU8jmdzzB7p9+5h2HXOuh1PGTOX+/idUNKuNgTDgr1knOfHXSX5YPj93Bsl6KCMj857x9vYmODgSO3szDAwUREfH8/hxOKdOBhBwfx6Ghkp8fZ1Yv+44LZrNZfDQBsTFJjJl8m48PLz56SedY74yZUrx9Td1WbJ8DsHB0YweuY3u3QPYtGnrhz7FHLNv3z7GjRvF5cs5dwb6obZh+U9RpEgRNuw9wLFjx4iNjeX7SpUy7HnWe8BAGrdoyZkzZ8hnacmQqlUxNHzpZGLpwqnMGOaENiGF5BQtSUlQv6oZx25FY29jy9AJMzAxNqFuq3Yka7U4eHiwoFq1165JygmGhob8uGIJ165d4/bt21Tz8sLf3/+t9/F7F6xbvQbDoCgkhQnhifF4mNswrGh1xl48iL2ZJX4li2Nra8vGPT/z77//8vTpUzoULUqhQoVyVU5gYCB//fEXSpWSOvXqvLZyvm/3XgrFOVLd3R8AU5UxvU2aMWfBKpq3bpHuXmfF48ePuX7kPF/6tEq99i08q7Py2n6uXs1yuVFGJHLrTMNeCHE2zd/LJElalov0SqAMugaRCXBSCHFKH+4PfKkfyfsB3X6V49Kk/QL4OTfGynzcFClShE37f03Vw7mv0UPfTPRwydIpTJ7uhEadRHKyRHIC1K5nxt8X4nCwtmPo2GmYmJhQu/kXJGu02Hh48NOkvNPD+cuWpephm49cDw0Co8HAnMjkeNxNbRlUoA5TbuzF1sSKwqV0erj5142peli0aKc30sM/9XpYNwd6uGfXPqye+FDMQufd0cjAhDqqzqyYv5iWbXKuhxd+v0w76+6p176mTX12n98s66GMjMxHj6mpKU2btWD3ru0UKzwCtVpLiZKe+JfxxtBQ17wyNFTy2+8j6dljOX16rqVAgQJ8OWgs3bp1Q6FQMHHSBFq2Ks3Xw3Q+QuzsLNi8dQD5vIYREBCQ632cPwRHjhyhd+9uLFvZnQYNRmJo8HoP6SA3QHOMkZERderUyTaOq6srLVq0yPRYZPgTvN2cOXrSmJGz7tKisgX+XsacvhDBrUQ7opYtoby1MZ7mxvwbEsU9Zy8aN84jJw3oRkCKFStGsWLFXh/5A7F/7z42zvuRlq4+qBQG/HjtL2o6F6Kuhx8WCkNWPTjP0u82AbqRmDd14b9p3Sa2/rSJ0gZF0KBh/dw1DJ36NXXq180yzcVT5yhh6ZMuTGWgxFFpzfPnz7Ncu5WWGzduUNDQLUNFt6DShauXc1HhQoA2V/7DQiVJKpubBK/wRJ9HHBAnhDgKlASOAU8kSTqtj7cdXYVLZ6UQSqAVusqazP8Rb6uHEZFP8fJ24OgRE8b+cI/mdcwoU1DFuZPR3HpsT8zKRVRyMsDLyojTT2O5b+f7Serh+u9/ool9IVQKAxbfOUh1hyLUdCmGucKQTUEnWb5wA/B2erhx7SbWztmKT2IptAoNK2atZ+SsIdTNRg/P/XMBd2X6Rq5SocJCbZ8rPfTQ+GTQQ7dkb1kPZWRkPko0Gg3R0dGsXbuWR48eseOXbazb0I9mzcsQF5fE2G+3sXH9cdRqTepenWZmxtjbWdGrdz/GjR2fLr9rVy/R6vOC6cKMjQ0pV74A165d+080QGfPns70ma1p1KhUrtJ9CC+4nyRKQ3MiopJZsjGQ+YPcaVvLhpr+5vw01BsX80gaOprRvVxRahf2ZVR1f1wjn7Frxy8f2uwMaLVaHj16RFhYWJ7mGx0dzarv5zCjel1quHvTwLsAs6rV40jwHe7FR/BMkcyin9e/tcevZ8+esfWnjfR36UQV53JUd65IX6cOzBs/h/j4+CzTuefz5Gl8cLowSZIITY7K8aiMi4sLQdrIDOHPpUjcPDLdyjFLJEnk+JMH7AaqCSGUemc5FYAbkiQ9Bx4LIV7URGujW5v4gjrATUmSPg5vKDIfDSqlORERyaxa8ow54134vJkVn1U2Z95kD5ztI2nipaR3tYLUK+HFuIZF8Uh8+Mnp4Yrv5jGpXBOqOftSx82PqeWacjzsJg8SwwhWJbJky9o80cO1c7bQxKgvJawrU8qyGo0M+jBrzPxs9dArvzuhKc/ShUmSRAzhudLDcGVGx3ERqlBZD2VkZD467t69S5EiBQkMfMxvB1cya9Y0+vSrRYuW5VAoFFhYmDBvfieMjFW0ajGf588jSUxMZumSw+zccZ5ePXtnyLNgoSKcOnk3XVhysprz5+7leiZLXnLq1ClGjhrB2HHfcuVKJp5Y03Dr1m0qViqQ6zLkBuh7ok37AbQZeA5jklFIGp4GJfMsVIuziwctKhnxPPZ/7J11dBRJ14efHom7E1c0SHB3d3d3d3cWlsXdXRZ3d3d3CCRAEoiTEM9EZvr7Y7IJ2QQIbFh4v53nnD7J1JR1z8yvq6pv3Zs53l6jAi6cTwvz8qtw6+YtWtZsxPTOwxnQpDMDu/UhKioqV+q+efMmZc2tyWNlRXRKEsnKVHRkMqo5OLM/7BWNOrbF2dn5H7dz6cIlikkLIpVkOGLRlemQV+LC3bt3P1uuZbvWXFY8JSguHIBUlZLjgdcpXas8BgYGOWrb09OTJGsZd0Kfq8PJiCLPIt7wXi+aChUq5PwkRNR7nnJ6fAVBEHYAN4B8giC8FwShhyAIfQVB6AsgiuIL4CTwGLXH1XWiKP71iGIQsE0QhMdAMWDmJ1W3RWNupiEbWrfqT4cWD9EhCalKSdD7FEKCVdjYONC4ppyQ2MxOQpsVc+DiiUOfqe3ncOvmTdrUrc/s3gMZ1qodg3v0ylU9LGVki42VNTGpClJUSrSkMirlcedo1DMad2qTK3p48cIlnJOKIRUy9FBbqkMeRd4v6mHrDq14o3+TiCR1yBmlqORe/Bkq1S/zTXoodVLxNOZRuh76xnrz0SJMo4caNGj45ejcuT19+pUnX/481KyVH0tLIy6cf8aK5WdITlY735FIJHh5uZOSbEY+91GYGPbm0AE/zpw5n63/gr59+rFzx23WrD6HQpHM+/cRdO28hjJlypI/f/4s+f8NRo4aTtu2zdDS9iUl9Sm1alVl4aIFn83v6enJ5Uve39yOxgT3X+JjZDjF8hogKlKxNJEiIhD+EZSpqSSliMglmdcCklJTc7SP5t8iNDSUP4ZPYrBLA4y11QOMR+G+jB04glV/rv/H9ctkMpJVKuRyObZODoQFB5OqiCcwIQaj4p4MHz/265XkpB25jBSyeulKIRW5XP7ZclZWVsxev4g5k2YS8ToUpUSkdrN69B82KMdtC4LA4vXLmTPtD36/tg0JkM+rEMt/W/NNnklzG1EU2+Ugz1xgbjbpD4FszdlEUez6T/um4f8nHyPD8fLQRxSTsTCRgohaD5WpJCWLyP/2e0hKUSKT/1p6OHfMeCYWr4SJrtqxzr33AYwfMowVmzf+4/plMhnJoloP7ZwcCA0KJjUplWBFNKZl8zIizePwP0Uul6Eka1gdpZDyVT1cum0uM8bO5ppfOMhUNOxah0EjBuS4bUEQWLZpKbMmz2bj5aUISChYMj+rf1+p0UMNGjT8Urx9+xYfn1d4edXijW8o69ZfZO789sjkUpYtOc3xYw85fHQECQnJ3Lzxkvv3H+Pk5IRSqfyiljo4OHDq1FlGjx7GoAFb0NfXpUXLlghCHHp6ukgkElq1bsGc2fOx/CT+9o/i9u3b7Nm9nXsPf8PUVB+Afv1rUKLYZFq2aJWt1c2YMRNo0qQBBgba1G9QLMdtaSag/xKnjm5nzfiCDJh6j4BwAXd7bWwsRPxCwth7NYX6ThmrxqIosvOJL/X7Dv9sfffv32fTsqWEBgbh4OpKzyFDfuhqyeF9B6lqWCB98glQ1NKdi77PCQoKwtbW9h/VX758eZZN/50GCfGY6unj5OpKdGIC/mFvaVW5Cl2atSMhLoGSFcvQd+iA73ZGUqNmDTbNXU+ZFC/05LoAfFRE4S8NpGTJL28Lyp8/Pxv2bEmPc/g9TkuMjIyYMf+P9BV/ieT7jBDEHARU16DhV+Xk0e2snlKQgVPv8T5YwM1ZGxtLEb+gMPafVNLMLcNbqiiK/HnHn/rdPj/pun//PptXLiYsOBB7Zzd6DBz2Q/XwyP4D1LdxTp98ApSwd+TEnUu5podL4mdSJzEeU119nNxciVEk8D7OmzZVqtKpcQcS4hIoVbk0/Yb1/0d6uGrmJgoqS6MjVZ9LTMpHIgz8c6SHfx7c+I/1cOai3zV6qEGDhl8WURRZuHA+cXEJDB60mZ49hnPm3Dh0ddWLovXqFaVU8YnMn3ecPbtvUb9+A1xc1D5DcqJpxYoV4/TpCyiVSpKTkylWrDBNmxfibcBClEoVs/84Sq1a1bh79+F3hKj6Ng4eOkDHzuXSJ58ADg7mNGpcnKNHj9KvX78sZcqVK8fu3fuZOnUCvXvmfAFWMwH9l1Apk9DVkTF1cGEmLnxMfnspJgYStp+NY/CYhVw8cQqfS/dxMNDhQVg0xWvWoVbt2tnWdevmTZaNH8cgr6I4ujnzKiycKf368duqVT/MZvxjeATWWllNq0xk+kRFRf3jAZeOjg4T5s5m2uixFDY0RQI8ionErVhxrq46TDeHyhiY6nLv9kt6t+3GloM7vjmkAYCpqSmjZo9hzrhZeAhOpJDKO1kIM1fO/uIq1ad87yDpU753wJZO7uxl0qDhp6DWQ1OmDC7MpAWPyOcmxcRIwo4j8QwZsYhLp07ifewZTiZa3AuMp1jVetSq9Rk9vHWTVdNGMrq6G84VC+IdFMn0oT2ZvHj9D9PDqIgI3HWz6o+ptk6u6eGk+bOYPnIchfQskIjwJCEcD69inF10guYW9dA30uPxuef0vN6dP49s/249nLhgFDNGzsYmwQOlkMJHg/fMWzNTo4caNGjQAOzcuZMLF47x0nc+u3fdxNTMIH3yCSCTSWnRqjTTp+2nbNnyrF27gffv32NtbZ1jHQV1jOkDBw7g6GTEzD8ywlnNX9ieiuVncuLECRo1apTj+t69e8fcebO5fu0KefLkoX//IdSrV++LZbS0tImNy2olmJiY8kWrzKpVq3Lx4jWAHGv5f24CGh0dzdGDhwkOeI9XudJUrVY1V819kpKSOH3qJL7ej3ByK0Ddeg3Q09PD3rkgj1/6USSfMZtml+GhdzRnrgVj4+hG9MePjJsxk8TEREJDQ+lQoABWVlafbWPtwgWMLF0iPRB8XitL+hcpyPqlS5izbHmuncunlK9Rmd3nl+Np6UqKMpVbQc/x/fCe24rX/PYPB1t/UaJkSXaePM79+/dRqVQMKViQrvXbMM6jNRJBPcgpZVOAD+9iOHroCK3btfmudqpUq0qZi2W5f/8+MpmM4sWLI5PJeP/+PUf2H8bn1StkEgH3fHlp2KzJPx5M5j655kxDw3+c6Ohojhw8TJBfECUqlPwxenjyJL4vHuPkkT9dDx2cC/DY248i+Y3ZOKcsD19Ec+5qMHnyeBAd+ZGx0/5I18PWX9HD9YvnMalOPvKYqFds89uaMaIybFy+iFlLVubauXxK2apVOHxtHsVsHUhWpnL1rQ8vQ0K4FhnEhFzUw92nj6Xr4YiCBelYuz0D8nRL18NiFp5EhkX9Iz2sWq0KZa+VyVYPD+09go+PD1JBgkc+dxq3aKzRQw0aNPynWL16Gb/NaIatrSnW1sYoErJuW/B+EURKipKiRYvg4GCLtrYclQrGjZ9Azx69OHz4MB8+fKBq1aoULlz4s209f/Gc8hUyR1wQBIEKFd148eJFjieg7969o1y50rRtX5Ily5vj8yqE/v27M3rMJPr17f/Zcm3btKVy5SX0H1ADZ2e1ye/Dh/6cOf2YFcub5qjtnPKfckLk4+NDl0atCdx2Heu7sZyauZE+HbuTlJSUK/VHRUXRq0NTAi8sppzWHaJurqJX+8aEhYUxaNgU5vyZxMFzYfj4x3HjfhCHzoTRtZgUx4iTTBnYlje+r6hSpcoXB1sAsRGR6ZPPv8hnZYW/r2+unEd2lC9fHnlBK9a/PMnYs6t57uOPWYIZFXSL0a1ZJ4KDg79eSQ7Q0tKibNmylC9fnrCwMOx1LNIHW3+R18AO70fP/lE7Ojo6lC9fntKlSyOTyTh14iQDmvXk+MLdvDtyB/MrIYT/eYVBLbtw7szZf9RWrvNX3LucHho0ZIOPjw8d67Xj5apH6JxTcWDiTnq175mretizfTPenVpOadVDIi+up2e7pmo9HDqVuZuSOXQ2DB+/OG7eDeLQyXC6FpXjGH6KKQPa5VgP46I+pE8+/6KAnRn+b3xy5Tyyo3z58ohuDiy9c4VBe7fz/OU78qQYUtMiHz1btf9hephHbpVFD111HHn+4PlnasgZf9fDk8dP0q1eP3bOOMSjzc+J36bk4ewX9GzQm7OnNXqoQYOG/w4fPnzAwVEdG7lJ0xIkJCSzfdu19K0Dx48/5OyZp+QvYM/Va8e5c38a74IWcfbCKFatXICdXR62/rmAZ8+PULdudfr07YVKlb1HtPz58nP2jDeRkXHpaaIocu3qawoUKPDZPj58+JCuXTtSvnxJevXuwcSJ42jTriSz57SlTBl3OnaqyJHjw5gyeRIKheKz9eTPn58pU3+jpNdkOndcQ5tWK6hZbTZr1q7/anzob+U/NQGdNX4aPWyqUMuxOJ6WLrRzrYJDuIz9e/bmSv3rVy2hbTEVvWo5USqvBV2qOdK3ojYrF8/G0dGRZWsPE63fkbUnbTl5UcWJifWpX8qRWsVsWdo5L5uXz/qi6/u/0DU24kN8fKa0txER2Dp8Pfba9yIIAj0H9yXKWkppk8LUcq5IpUJlaZ6vFvXkpVj4+/xcbzNPnjwEKiIQRTFT+tv4UNwKfLvL58+hUChYNn0RdQ3LYSQIDM1bj0qWnhTWtmWQS3WWTJ9NcnLyV+uJi4vj/v37vH//L3jZF7/h0KAhG34fO52Weg2oZFWW/KYeNLaug4WfAft25064k/Url9Imn4QeVdwo6WZFp4ou9C5pwMpFc3B0dGT5msPEaHVi3RE7Tp2DE+MaUr+kI7WK2bG0Uz42L5+dIz3UMTQhPCYxU9rr0Cjy2DvlynlkhyAIdB/QnxBjPSpZ56dhvjJULVqa9kUr0dqsAIv/yOKf5h+TJ08eQlLCs+jhu6Qg3AvlXqw4hULBgslLKaGsjm6yNq0MOlFEpxROKe40lbdh/qQFGj3UoEHD/ytiYmJYu3Yt06ZN48yZM5kmiFWqVmfXTnV4Xz09bdw9bJg6ZR/2tgMplH8UQwdtYdeeQfi9DWPTlt44OVkAUKCAHUuWd8TCUo8jx4ayfGUXnr+cxd07F9m9e3eWPpw4cYJp0ybx/Jkfzo5DaNZkAS+ev2f40G0kKaTUr18/277HxcVRu3Z1CnqK/DGnPg6OsRw5eoimzYpnyleggB2Wlka8evXqi9diQP+BPHvmTdUqHWnYoBe+vm9o2aLlN13PnPCfmYAmJSURFRSOvVFmL1IVrQty8VjurOjeu3GRmkVtMqWVK2DJi0e3ATAzM6Nb974UKlyWAbVdMNTLsKfW1ZZR2kWXx48ff7WdbgMHsfD2PT7EqVdIgqKjWf7wCd0G5dwj67fg7+9PuwbNWdxvMs+v3CevlgMGhoboaOsAUMDMlRf3vxwn6HswNDSkQv2q7PW7hCI1CVEUef7hLXeUb2nUrEmutfPs2TNcpba8+viaCubu6fbrehIdlInJuGlb4O39ZRfTG9dspmXltizpuY7BjUfSt1N/4v+2SJCbiCohx4cGDX8nKSmJj+8iyKNvnSm9pGkxzh89lytt3Lt+ieqFM8dzLOthxYuHd4AMPfQsXJYBtVyz6qFTzvSwS78hzDztTViMerL6PjKOeRfe0LX/kFw5j7/j7+9Ph8ZNWTVsLK9u3MXT0AoDQwN0tLUBKGztyMuHX+/3t2JoaEilRlU4Gno6XQ9fRvnyRMubxrmsh9YpTvjH+VBYXlS9PxPQFnVJiU/FRmn3VT1cv2Yz9Sq1YWK3tXRuOIIeHTV6qEGDhl+TBw8ekC+fO6dObyIp5QEjR/alXLlSVKtWCUdHW148f8afW24yaMAWTp58RGxMIh/CY+jdpzrbdw3C22cehka6JCYmkTdv5lArRYo4EvEhNv21gYEOQ4fXYufOrZnyPX36lC5dOrBsZVvCI1cTFLIcGxsTKpSbRlycFWfOXPjs9pj379+xbEUnRoysT8WK+ZgwsQmurpb4vArJlC8+XkFwcORXrYoAbG1t6dWrF127ds31J59/8Z/ZAyqVSkkRlYiimGmDbGxyIobmhl8omXO0dXSJT0rF6JOBVFKKEqks88ZdAyNjot9kffwenahCX18/S/rfqVKtGgALVywn9mMUplZWDJszlyJFivzDM8iKSqViVO/BdDAqg0MeK4L8gzGS6hDyPghnd1dkMhmpKiVS+Y/5Kg0fP5o/bbewZPt+UpKSye/lybKxazEyMsq1NvT19UkQFVhIDYhLzXiSohJVaEmlxKUqvhjb7ubNmxxZepJ2Rv2QpMXTe/r4HtPH/86sxTM/W+4foTEl0/APkEqlpJJVD+NS4jEyyiU91NUhXpGdHmZ2ymBgZEy0rzJL+WhFDvWwajXgN6avXkp8lC8mltYMmrHoh+nhmH4D6etYAGdTC4LfBWIs1yY0MAgddzdkUikpKiWSH6SHIyeMZGuerWzetpvkpGQKFi/EyvGrc10Pk4RE9CSmJKgynkCLqJBKtUgk8at6uHXxCYrr9UnXw3cP7jNl/O/M0+ihBg0afiKiKHLw4EH27NmOKIo0a9aa33+fxuy5LejQsSIAU6Y2p27t2djZ6bBu42guXnjOmFFPiIu1ZNH8W3ToUIAuXXuyYtlWQkPiiI9P4sjhe9jZ2XD+/DNq1PBMb+/E8YeUKOmaqQ/a2nJSUjLvI121egUDBtWgZk11WQMDHZYu78L+fXdo3KgZFhYW2Z5PSkoK8fEJdO+V2Wv5mLGNGdBvAyVLuVKokD1xcQqGDdlGrVo1sbGxybauf5v/zARUJpNRsko5rjx8SmU79QbgVJWSQ0F36D38n8eYDAwMJCFepPHYC+Qx06Z2WVu61PZg04X31GrUNlPe2nXq0X/LcmoUTsLMUBtRFFl70psT1/x4FdIfS1tH+gwbh6en52daU09C/5qI/kiePn2KdYoeDkbqFZOqrkU5+fom9czKERMTg5mZGWeCb1C3dYMf0r5EIqFz96507t71m8olJCSwdO5irp6+AqJIqaplGTp2WLYDtXz58hFnnEJhuQ1HXx3Hy9QZXakW8aKCKJWIwkQr3aV2duxav4ey8hrpgy2AQgbF2XZlGcnJybkfz/WvPU8aNHwnMpmMUtVKc+vqfcpalABAqVJyNvoKg7p/PvxTTgkMDCQxUUmLmcexMdGlppcjnarlZ8s1P2o1bpUpb4YeKjAz1FHr4annnLj5Fp9xfbGwcaLP0PFf1sOq1dImoj+Wp0+f4oAcZ1P1YKBWvkIc8H1EC4eixERHY2ZmxpE3D6ndrPEPaV8ikdClZxe69OzyTeUSEhJYNHspF09cQRShfI3SjBg/9LN6qLJOxDKpEJcjDuOhyo+WoINCkkCqPBHBRvyiHm5dvwcnSbVMemiv58Wtyys0eqhBg4afSt9+vbl54zwDBlVDIpHwxx/jefculLbtyqXnkcmkjJvQmMkT9+LsbEnXblXQ0ZGzfu1jzp+/wsWLF+natRv9+w1gwIC+3L59Bz09HeLiEujQdgULF3ekVGlXzp97zuiR25k+M+Oel5KSyorlF+jQPrPF4rsAP6pWy5spTSaTUqiQHX379qZhw4bZhmCRyWTIZFL8/MJxc8uwaHJ0Mkci0aZW9bmYmuoTFhZF7dq1WLt2Q25dyn/Mf2YCCjBy0jgmDR/DvGeHsNIy5q0inBY92lOuXLmvF/4CMTExDOvejd75nLD2sCM6OpIjD99S/cJl6rdqR9+uPTPlNzU1ZeiU+QyeMZZ8lhKevg5FX5nIweE1sTA24G1YDNNH9WXGiq1fvNH/GyQkJKAnZDyxqOJUjNC4jyx+uwvnZGcSY1QUruRFz369f2IvMyOKIkN6DMTpvSVDrDsBcP/aUwZ07sfm/VuzhA0QBIH5axczqu9wZGZGjHq6Ayd9M/QtTZCbmTF7xaIvupWOj4tHR6qbpU4ZclJTU3N9wCWiMSXT8M8ZPWUsE4aOY+2jbZhLTXmvDKZ1v7a5oofDe3alf2FbbArXIio6kkPe76h9/TT1W7Wnf7fP6OH0cWo9fBOCoTSWI5MqYWGiz9uQWKaN6830Jdt+DT2UZtw2a+YtSHBMFL89OYmrjSOxOgIFypekR99fSw/7dx2MzhNHGuoPBODVoQf0fjaA7Yc2Z6uHSzYuYGjPkeim6LE6dCHWUmuM8hhhZKvPgjXzvqyHsfFoZaOHErQ0eqhBg4Z/lQ0b1zNv7mx8fd/i4eFKVFQEz1/OxsBAvYWsbbtyeLgO49ZNX8pXyAjbJYqZw4nUrlOEAf0ym80uW74YHd14vH3mEhoaTd1as3Bzt+GP3w8RFByFro4uEydOZca0WTx7EoSjkxl7d9/DySkfXbt2zVRX6TLlOXjgFM2al0pPi4iI5fHjAJxdbLlw4QK1atVKf0+lUnH27FlevnyJsbExgwYsZduOvmmTzWiGDdnBqNFjGThgID4+PlhZWeXI9Pbf5D81AdXT02P+qqWEhIQQFhaGu7t7lthpCoWCDatXc/nkSQRBoFqDBnTt1euLN83DBw9Qy9qUonZq228LSys6mVhw9fAJ7t+6yO9Tx9Cr/wjy5MmwDS9TpizbDp7D29ubR4N7sbR7BXS01B+Hi5UR/cpbs2TuTFAqCXnvj4V1Hrr0H0LJkqWy7cPniIyMZPWiJdy/cRMdXV0at29Lyzatcxynp2jRosxMDCYwJIik2EREUaSKWQFeqz7QdkpvypUrl2P78CuXLrNp2Vo+hkfgWsCDfiMH4+bm9k3n4+fnx/I5y/B+/BITcxO6De5G9RrVM+V5/vw5Sn8F5fMUJ0WVyt2QR3h/8CXAL5hDhw7RrFmzLPXa2try56EdvH37lri0vbWGhoafHfCKosiqFavYuHg9YeEfCBI+0tyjA3K5+nsSpgjG1MH4u2LzadDwb6Cnp8fCNYu/roerVnPpxCkEiUD1Bg3o2rvnF/XwyMED1LUzpKiD2szHwsqKLqYWXNl97st6eOgs3t7ePB7Wg2X9imfooY0h/auZsXT+76BKJeS9P+ZWtnTpO/S79HDt0kU8uHUdHV1dGrbuQIvWbb5JD2dHhxMYEkJSbByiKFLbzhVvRQxNxg7+KXq4dNZyXjx6iYmFCT2Hds1WD2NeplLSoCypqhReRN/jbbQ3HyLefVEPdx3b9k16uHL5KlYv2Eh4xAesxAgquLZP/55EK0KwdjbS6KEGDRr+NVatXsniRbNYuaYzJUu6MqD/Rgz0ndInnwC6ulp07FyReXOPsz9tApqSksq8OUdp2ap0er5HjwJwcrJPfx0bG8ufW7fh7TMbS0sjmjddyMxZbenWvQqg1sS2rZeyZs0K5s9fRGhYKB8+hDN7dndq166daeEvIiICG+s8zJ37kOHD/qRzl0oEB0cxddJeevSqhq9PBB8/fkzP/+HDB+rWrYkoJlCmrCuehRrw0jsUD9eRODtb8/ZtKL1692LY0GFIJJIvWg/9TP5TE9C/sLGxydYGWhRFhvftS9FkBXPKlkQEjly5xJgnj1m4avVn63vn40M5M5P019FRH0lVfKSCszHd2poQEfOIEf3asWLTQUxMMvJJpVI8PDww1pWmD7b+wtlMj+tbjrKmW23cSxcmICKGOZOG0XfqPMqUKZuj80xMTKR/xy40NLajlVd14pOT2bVpF0EB7xgyemSO6tDV1cUwjwUzr+yiiXUpdKRanHp9nmgzkXr16uU4ZuCp4yfZOn05HZyqYuFizOvgQEZ07s+SHWtxdMyZ996goCAGth1ATUlNKhpWJDommlUjVxE7MZYmLTKccPj7+5MHS1JVqSy7vxE70ZJy+kVxTsnD4jHzMDU2oWr1rOZ6giDg6uqaJT07hvUfypXtl6lnUAd9A322hu9k7aPFVHevS4wQia/+E5bMW5ijur4HTdw7DbnFl/RwWJ9+eManMr1YBUQRjp+7qtbD1as+W1+A7ysqWRinv46O+ogy8SMVXIzo0tqMiJinjOjbjhWbc66HLlbaXFt7hE1DK+JRzwX/sDhm/jaI3hMWfZMeDurWkfZuhvRrVIS4pGQ2HNzAsncBDBo5Okd16OrqYmBtzfizR2jr5omOTM6RR7cI05d/nx46VsXC2YTXwYEM79yfpd+oh31bDaKMoh7N9WoQExTF4sFrifktlqZ/00OTpDwoZansebMCY4Ut7kJZzJTO/DFkESbGplSrXjVL/d+ih4P7DuPEpqsUFRpTCH0upWzi+NMllHCtS7I0khjDZ6ydr9HD/zKCIHiKovj0Z/dDw38DlUrFzN+ns//QALy8nAEoV86DM6ezOswMCY7l4oVXNGuyhLz5rDiw/y7x8QoWLlZb0D19+o7BA/6kefNONG/eiJo16zJ37h8YGeliaWlESEgUPq9C6NylUnqdgiAwakxDmjdZyOjRw1iwcCmjR2W9z6xes4qxY0ZTsVIBLMyN2LfnFqdPPcbUVJ++/WtSt14RChccz6qVldPLjBgxhAoVbZm/sD2CIHD5oi5Vq+VFLnelb5/+uLq6YmpqmstXNPf5z3jBzQmPHj1CL+IDTQoVQEsmI1ahwFpPl5BnT7/o9S9v4SI8CY8A1IO26I8fsDGV4xcbi4OVPuU9LWlRSmD/3h0AKJVKbt++zfnz50lISCBVqkNMQma39pce+VDZwxZ3a/WXyNHciAk187N+yZfDnQQFBXH27FmePXvG8SNHKSE3pryjOxJBgqG2Dj2KlOPqsZPExMTk6JoEBQWRGpTA6LL9iNKR46OKpZhdMRylNlw4fyFHdQCsW7SCnm51sNQzQRAE3M3saWJago0r1n61rEql4s6dO0wZNwWvFC9cjVwRBAETbRNaWbRk/aL1mUIT5MuXD39VEHdCH2ErWlLPrAp22ta46DswwKkji6bN/2wMppwQGxvLse3H6Gfeh/y6+XHQdmCs3Qi05MncNj1D+TFF2XV6Ox4euRcqJhMioBJyfmjQ8B08evQInbBIGuT1REsqIzZJgZWuPkGPn39FD4vyOES9WiuKIlEfP2BtosXb6HgcLPUpX8iSFiWkn9XDFIluFj28eO8N1QpZ4mGrntg6WRkwpbkzG5Z/OdxJJj08eoSKFlKq5LNHIhEw0tVmSBVPbpw++k16SFgMv9XsSqREF+/EZIo7FcZdx4IL58/nqA5I00PXuljqmar10NSepiYlv0kPJ4+dQt7YkjjquyEIAsZyU+rotWXNgg1Z9PCDVgAvYu5irMhDCWkdzCW22EidaajXlzkTF/5jPTzw53FqSYdgJy2AhdSR5toT0UUgwvI8bSd4cfisRg81sEoQhNuCIPQXBMHkZ3dGw/9v4uLiiIyMSp98ArRsVYaLF15w7lzGOsiVK94cP/aIBw8e4uFekdMnX6Ml18fVJS+VK/xOHqtB1Ku9kGrVGrBu3Upq17XCxcWcGrWsiIiI4vnzQKRSCSqViFKZWUeTk1NRKlWYmuowoH8f3rx5k+n9J0+eMGXyBG7dncrBw4N58nwmVtbGmJrqM3BQbRSKZKpW+oORo0anLxKrVCp2797HhEmN0y13BGDCpMYcPnSIEiVK/E9MPkEzAc2Er68vBQz1EUWRLTdvM/3Icd76BWAQF8/wXr0IDw/Ptlz9Ro24GZvEeZ83KJKSiU9NZuGtJ9Qob42ejnolv5ibIa+9H/H27Vs6NqnH6cVTePbnAvq2aoi7Z3GmHPEm4EMsoihyxzeUxef96FsjcwyfPCYGxEV+yLYPoigy57ffGNupM49XrWX96LEs+eMPHPUzO5kQBAFXQ5Mcx2bz8/PDSW6Dha4pelJ9HgUGEPhWSby/jHH9J/D27duv1qFSqUiNS8JAK/O+oLxmjvg8e/nFsgEBAbSs1ZJV/dcQeTKKc68vcDnwSvr72lJtJEmSTIF13dzcsCnuwGn/yxTWzYsKkZiUOCTaEsyNzDBKNVAPJL+T+/fvYyezRUeSYcYhESTUMKqOqFDSrmPbXPVKmR2imPNDg4bvwdfXl3x6RoiiyLZ7N5h9+hDv3r/FID6OYb16f14PGzbi+sdUzr54iyIpmYTkFOZffUH1MjYZeuhqlKGHTetxZtlknu+YR7/WDXEvWIJJu94QEKY2cb39MpxFxwMZ0CizGZGtuR5xUZ/Xw7kzpjGpVzt8dy9k629DWT7nd9xMM5uACoJAXguDb9JDNz0LrAyM0dfS49G7AGKCFRCuZMqQcf+KHjar0YrFPdcTeCiOa+/OcSv8Yvr72lIdhERpFj10LWfDzfCzOOGJKIokKGOR6QmYGpqjnWD0j/XQVHRALmTooSBIKCyrjTJBSXuNHv7rCIIQIwhCbNrfmE9eJwqC8P2rDf8AURQrAh0AB+CuIAjbBUGo9ZViGjR8kaCgIIYOG4yXlyc1a1Zh9+7diKKIgYEBJibGPHrkn57X1FSfwUNq06LpYsqWnk6Fcr/TttUqpkz5jdmzZ7F33zZGjq7Cxi2dqVLNFh0dHY4dO0VAQCA3b15l9bpu9OpdHV1dLYYMrUvLVmWoX2cON677ULCQHYsXnUxvKzVVyayZhxk0pA5PX8xhzNiGtGnTIlPft/65he49K6c7D9LSknH1+hSiPipYsewOt28ms2rVZiaMn5heRhRFlEol2tqZvcnr6MhJTs7sWfdX5z9pgvs5XF1d2R2fwJ2Ad/gHhzK7UiUkgkBIfDxBUhkzxo1j8bp1Wcrp6uqybPMWNq1ezahLF/B5+ZQpPdxoWCEjBt5Tv1ic3Qsxefggxpe3x8XSBIAOpZSMOnKDet2GsOTkQSLDfcnrWZQiZaTEJ6UgiiIpShVyqYSwmAT0TMyy7fuJY8eIunWHmRWrpq+KnHj8iK0PrlHKIcOMShRF/GKjsLOzy7aev+Pk5ERASiiBcaFcfPWY9rq9kQkyolJiSJanMLL3aPae3v3FPVQSiQSpnhYJKQr05BmDFN+P73HL/+UA6qP7jaGGoja2pnYEJwSjilZyMHA/LsbOOBg4kKRMIlUrFe20GHx/MWvJHLqGdCLgURDaKm2MzY0xt1DvzYpNjftHAyI3Nzc+pkahXnrPOO+I1EhsnXN2Xf8pIpqVfA0/FldXV24lxnHvvT/vw4L4o3xlJIJAaEIcITLJF/Vw6aatbF6zimEX1Ho4uUteGpbP2D+j1sPqTB4xiImVbHGxUj/Z7FhGyYgD16nXdTgLTx0g8sN78hYqSpEyusQrUtV6mKpCLpMQFqVAz+gzenj8GAnPrrCkebF0bTpyW8Lqiw8o75HRD1EU8Y2I/yY9fJPwgYDocC56P2W0awvkEhkRSfHEGoiM6z+CHcf3fZ8eRn1dD0f1HUvpyPrY6NoTbBRCykcVZ0N24WDgiq2uI8mqJFTaKVn0cN7y2XQM6Uz42XdoSbUxtjDGwtIWgERi/7EexouR/F2SYlQfcHCz/e56vwWNHmZGFMVMH6ggCAbAAKA3sP+ndAoQRdFHEISJwF1gCeAlqH8s40VR/Gn90vDrc/z4ccLCwqhQoUK6NUVYWBjly5ehSbPCrFzTmncBEUydPJpXPi+ZOGESY8aOo3OHBazd0J0SJVw4d+4Z69ZeZevW7RgbGxMVFcXcuX+wZMlswsIiuXpjKoUKqe8PpUq5IYoimzZvwMtrKQ8fPqN+/VGZ+tSwkRd799yie9fViKKI94sg9u29jZeXE2fOPKVgQTuGDa+HIAiMGNWAFcsv4O3tTf78+QGIj4/D2SXzoqi2thzPwo40bNCbzp07Z7kOUqmUBg3qsnzZWcaMbZievmTxaZo2/THe138UmgnoJ3h5ebHKwIgtt+7Qr2BBBEEgSqFAlMkp4+zMnosXiYuLyzYGmqmpKcPGjoWxY1m/ZhlPHmynSkIqhnoyHvh8ZNcNJSOnVODl2cPpk08ALZmU5gWs8At8x9L129LTHz9+zKge7dBNjcdAS0pckpJkmS4TFq3Jtu/H9uyhe/6Cmb12FfJk5e2b3Hn/lpJ2zihSU9jz8iEla1bD2Ng423r+jp2dHc6lPNh05DBFpeWQClLiUxNJlipxsXDnaeRd3r59+9W9Ql0G9WLD7A10dK6KiY4hAdGhHIy4w/x+Kz9b5v379xAOtibqwaG5pTn+Mf54yUtwP+wBJlomHPt4jM6jO2fx5CiTyZix4A+GtOpPxTzlMZCrf+S3wh/iUSr/Pxpw2dvbY5Xfiutvb1HWsDQSQUJEUiRnk86z97e9311vjhEBpWbApeHH4uXlxUoTfbbfv0GfAoUQEIhWKBDlMko5O7Pnyvkv6uHQMeNgzDjWr17Gk3s7qJKQgqGenAe+key6nczIqRV4ef5Q+uQT1HrYopAVfkHvWLpue3r648ePGdqnNXpCDIa6EmITVCRJDBg/e322fT+5bxdDSjpn0sP6JfIy/8xjrr8OpJyrLYkpqWy+7YNXlZrfpIf2XgVYdeo0VUwLI5PIiEtRkCSoyGfjymW/lznTw4G92DBnAx2dqqn1MCaUAx9us+ArepgSLGCjqx4gWVia4xfjT8GUMjyNuIuRjSmXEg/RbUL2ejhr8Ux6NR5MaWlFdGXq+Kov4u7gWS3vP9ZDB08LvB9fJZ+sAgICscoIXshPcWL67u+uN8do9PCzCIJgCgwBugDbgFKiKEb+pL4UAboBDYAzQCNRFO8LgmAL3OAnTow1/Lq8fPmSp0+fsG37SlxdLRk9ejht2rZjyeJlLFu+lNp18rNgYQdAPWksW86DIoXGM6D/QAYOGIRcLqdT+9m8eRNAkSIFWLR4BU2bNgWgVatmlCpjQfcezWndYnH65PMvmjQtzsB+e5FKpVhamvHyZTAFCqjHozt3XGfMqB2sWdeTYl7OnDn9hBm/HcDd3Zod268zcUpzRoyon34PkkolWFgY8fjxY6ZOncjFi5fQ09MlKSmBvv2qo6enXowMDIzkzOnHLF70eeOABQuWUK1aZe7e8adMWSfMTStz6MAzLl688tkyvyKaCegnCILAorVraVWnDuGJCrRlcRgaG2NvaQmAVCLJ0V6Z7r0GsH+vOUM3byZZEY9z3sLMXjaOxMREZJKsVs8yqQRlauZH57GxMdia6DGwpBPaEhXJKgnrH0UQ9YknrE9RpqYil2R2gCGVSnFwcuSpjR67755GS1ub+q1a0KHrt8WQmz5/Jh3fdCLmeiKhKZHoG+rjZK0e3EkFKampqV+to1GTxujo6LBp+Tri38dg5+LIHxsWf3GglpqaikTMuF5aWlo4uDgQ8jaYu4l3ideOp/P0ztRvWD/b8s7OzoyYO5YFU+dilKJHTGoc+coUZOqs377p/LNj/9kDdGjagXkPFqKLHgp9BX9s+oMCBQr847q/jqCJe6fhhyMIAovXraFl7TpEKBLRlcdiYGSEQ5ord5mQQz3sPYD9ey0Y+ucmkhUJOOf1ZPZytR7Ks9FDuUwgNSXzHtDY2BjsrbQZWscBHbmK5FSBVReSiPqY/Vg6VZmCTJbZxFUqleLo5MRV7Fh/8D5yLR3qNmtF+y5dc3hF1EybO4sufu2J9lUQlBiNnqE+TtZ26tBLgiRneti0MTq6aXoYGIudiwOzNuZADz/ZNSPXkuPo6sAH/yCeirdQmcfQc1CnL+rhxCUjmTVhPtoJRiSIMRSunp/pc6d+0/lnx9Hz+2nTuCPH7lxCrtJDME1g8crfNXr4kxAEwQoYAbQG1gNFRFGM/bm9YhmwFvXTzsS/EkVRDEp7KqpBQyZEUaR9+9aMHTucq9cnABATk0DNanPYsWMHt25eZeCQkpnK2NqaUqCgA0+ePKFy5cr07dOPvn36IYpipgXJmJgYTp06Q0DgIhSKFMLCYoiNTcTQMOO+8eplMHny5OHFixcULORJw3rzWLSkE8ZGFRg/dhc7dg+ifHl17M78+W2RySQcPfIACwtDLl98wYgRGVp8//5bAgMjGDx4AEOH12T2vEm8exfJ0MFbKZB3NKPGNCQ2VsGqFReYMHFSJi/xf8fFxYWnT1+wbfs2Xr70xt3ViocPn/zPeRnXTED/hp6eHkMmTuTSsmVUyZsRFPb1hw/oWFrmaKVYEARatGpHi1btMqWrVCr84lWExyRgaaSXliZy2DuM/l0aZsq7fc1yJtQvgq1pxtOFCQ6OjNu0hgaNsj5mr9m4Mcd27qFzUa+0FJGbvr5ExMYSGRZB+359aNKsabaBbL+GXC5n0qyJTO4wnWrGtZAI6kFQdHIUMXqRuLt/2WzsL2rVqU2tOrVz3K6TkxOJholEKiIw01Gbz2praxNo8Z6N6zdSvHjxr9QAlatWpuL5ioSEhGBgYJBre5EMDQ05fO4wMTExxMXFYWNjk+Wpw49EY3Km4d9AT0+PoZMmcnHJcirnzXAi8ybiAzpW36KHbWnRqm2mdJVKxds4kbCYBKw+0cODz8LpP7NRprzbNyxlSgc37CwzbrBT7ZMZ8ecqGjRqwt+p3qAZB05spHel/OlpV5+/5UN0HFER4bTp2Z/GTZt9tx6OnzGFOb0nUs/DLV0PIxNjCJXE/VA9TDFJICo6AhMttR5qaWkRYevH1i0bcqSHVapWptKVH6OHxy8c0ujhr8MbIAJYByQCvT8dfIui+GVvhj+G/aIoZgqkKAjCEFEUF/89XYMGUD/9DA8PwcLSEPXXGIyM9Bg1pi5bNm3E1s6Bl97B1K9fLL1MSkoqb9+EYmub2fz/79siEhMTkcmk6OtrY2ioS736xRg8cDNLlnXB0FCXZ8/eM3rkduLjUyhduji9+tSgfPkK9O65jgkT7AkPj6FcucyO1WrVLsz4sbvo2r0Kly+9oHHDebRpW47Xr0NZvfIiZcuWx80DRo1Wj/eTk5VM/a0F7dss5/atZMzNLdm37whlypT56rUxMDCgT+8+AFy8ePF/bvIJmglottSsVYtr588z/do1ypibE5KUxL3YWOat/bqHwi8hkUgYN3MeY0YNprqjIcbaUs69/UiJus0pVKhQpryREeHkMSmYKc1ET5vkhLhs627aogVjLl1i7q3reJma8ej9ey6/fsPoSs2x0THl/Pr9XDlzjoWrV+Q45t2neHp6Uq1zJXZtXY9HqicKSQJvdF4we9XMHzbQEASB35fNYGSPUXjE5UVP1MNb8KZiqwp4eXl9vYI0JBJJFjHKLYyMjH64g41syUVvjoIgbAAaAmGiKGYJGJW2R2cxUB9IALqKong/7b26ae9JgXWiKM5KSzcDdgHOgB/QWhTF7B/fa/ilUevhBX6/eZXSphaEJim4nxDLvLXZbwfIKRKJhHG/z2P06MFUd9LHVEfKmTfRlKyTVQ+jIsOwtcj8GzYx0CJZkf1DnabNWzDuygWmnnhCGVt97r8N4dyzd/zRqjr2ZnocObSR0efPMn/5qu/WwzKtarBw935K6LkRr1LwIMWf35bP+aF6+Mfy6QzrNhrH2ALopOrjp/2MKu3KafRQo4d/ZzbqXbkCoP8D2/kWOgOL/pbWFfX1+m6+9nlp+N8lMTERAwPdLMtLBoa6JCYm0K/vQJo0qU+58h6ULetOQkIS48bsplgxr68uBFpZWeHk5MjBA3dp3qI0K1d3p3/fDdjnGYiRkS4xMYmUKOnC8JENGDp4C5Uq5aVJ05IMH1mfbVsSkMmkvHsXgaOjRXqdG9ZfIm/ePCxc1ImEhCS2brnKieMPuXXrDSNHjuPs2ePUqFmKjx/j6dxxBffv+eHsYkmqMhUDfQOWLF72A67ir4tmApoNEomEabNn4+3tzb07dyhmacmw6tW/GHw9pxQtWpSNB45z/vw54mJjmDymcrZx3zwKFOahfwhezlbpaW/CorCyyz5GnEwmY97y5Tx58oQb165xf8U6NjQfhJZU7Smrbf4yrHx2ifv371OiRIlv7ndcXBwu+Zxp0Kc2ogrsHeyoXmMa+vrZ39sUCgWXLl0iJjqGsuXK4uDg8M1tAhQsWJC95/Zw4fwFoqOi6V2h52eDoX8PL1++5OGDB9jkyUOFChW+64nIv41Irntz3ITaPGrLZ96vB3ikHWWAlUAZQRCkwHKgFvAeuCMIwmFRFJ8DY4FzoijOEgRhbNrrMbnaaw3/Cmo9nJWuh16WlgzPTT3c/4kejsxeD93yFeGBjw/F82Y4HXodGItFHuds65XJZMxZspInT55w8/o1bl1bybFhbdGSq7cp9KpYkJlnH/8jPXTycEHauSGIkN/enlE1qv8renjgwu50PRxeoUuu6+GDBw/Io9HD/2k9FEVx+o+q+1sRBKEd0B5wEQTh8CdvGaJ+SvtP2cSXPy8N/6MULlyYuLgUYuMyvHqrVCrWrLpIg4atKV26NIuXrKBd66FIpRAVFUf16tXYtm3jV+sWBIGlS1fSrFljVq44x6uXQYSFxaCrq0XDhl506lqJkyce0bfXOiZOasbiRSdp0rQkxsZ6mFtIKOTpQK8e69i0pQ958pjy7Nl7Vq88R+++NQDQ09OmT98a9Olbg0kT95CYmIirW17u3/Nj4/qLODtbsu/AMLS0ZNy985r27VaxZ88eWrVq9cOu56/Gr393+Ynkz58/3VtVbqKvr0+jbMxoP6XnoGGM7t2J7kmpFHW0wDsoktW3g5j0GSdEoP5BFSlShJiYGKo5FEiffP5FEQMbHty++80Drls3b/H78EkUk7ugLci4p3hNq/6dPjvY8vb2ZkT3Ebglu6Gr1GWbZBs12tdg0IhB39TuX+jp6dGgYYPvKvs5VCoVE0eM591NX/JJHLklxLBEZyHLtq78ou39L4GYu4HXRVG8LAiC8xeyNAG2iOrggjcFQTARBCEP6tV8X1EU3wAIgrAzLe/ztL9V08pvBi6imYD+T/NT9bD/CMYObE/vxFS8PEx57h/N8pPRTJg967NlPtXDBp5O6ZPPvyhra8TDu7e/Qw9vMmv0BMoY2qIjSLke9Z7mfbp+UQ/H9B5KYak9BoI2exduonLrugwY9mvp4YThE3h92Q+nVFdiZFdYaLKYlduWa/QwK7+8HgqCsA8YJYriG0EQVgAVgemiKO75UW1+getAMGABfGr6Gws8/qeV5+Dz0vA/ikwmY82a9bx86c3woadxdbNk7557iCoD+vfrD0DrVq1p3qw5b968wcTEBCsrq6/UmoGpqSkODvaEhnxkz/6hFCxox9Ej9xk25E969K7Gb9NbIZFIuHLFmwD/jLUSlUqkStX8pKQoKVxwDFKpBJDRslU7rl+7mWm/qSiKXLn8mqFDOtGiRQsqV65Aamoy74KW4uMTQu+e6/D1CUGlEhk0qC/58+encOHCuXodf1U0E9BfFEdHRxZs2MH2jevYe+Uxzu55mbl6Ns7Ozl8ta21tTXByVtO04KRYvOy/LUxIcnIyM0dNYaBDE2SClBRVKlW1irNwxZ9Uql4Fe/vMXsNEUWT8gPG0kLfA3FC9T6msWJad23ZSpVYVihQp8k3tfwmFQkFUVBSWlpZIpdKvF/iE40ePEXszhJ4OGXGZfKL8mDF2Gss3r8q1Pv4iWAiCcPeT12tEUfwW+0k74N0nr9+npWWX/tfmBWtRFIMBRFEMTnOKoUHDd+Ho6Mi8VbvZvmUNO3Y/wsm1GDOW9M2xHgbEKLKkB8Qk4Wpnn02Jz5OcnMyssZMY51kdmSAlWZVKLbfCTF+zmYrVqmarh5MGj6G3dW2s9NXBwauJxVm59wiPa/w6enjs6HHCLkbS2rxjetrb2DdMG/0bq7Z+3jPv/yj/BT30SJt8lgLcgTrAaeBfn4CKougP+APl/u22P0UQhN6ow9BgaWnJxYsXf2Z3MhEXF6fpz2fQ0dHBxcUVU9O2xMbG0LxZMeRyGUeOHMHa2jpT3qCgIJ4/f57+OjY2lpiYGKRSKebm5sjlGQ9l4uLieP3al06dulKokD1JCTIe3AW7PDVYtrwUzx4rSIzTpVzZ1lhZhFK7ljZXLuqSnJKKllxOXvf6aOvIcf6tDvr6BumWKC9eeLHjz0RsbEwACAmNpl3bvpiZmREeHs6aNesJDHzH3VvGPH0ay4gRE7GwMAQg4kMsFy6cJzg4+JssjH6lz+tb0ExAf2FsbW0ZOWHyN5fz8PBAZW3MzUBfyti6IQgCryNDeJj8gZG1an5TXY8ePcJWZcyGBweJTYxDSyIjWVBR2MaDsydP07Vn90z5/fz80I3VxdzUPD1NIkgoLi/Oif0ncmXApVKpWPDHIs4dOIchxsTJYug9qhdNW2R1RvI5Tuw9RlXzzN7TPEycOep9haSkpCxx9H41xG/b8/RBFMWSX8/2WbJrTPxCugYNuY6trS0jx0795nIeHh4oDK24+PI9VfKqvdW+DI7gWqiCXjU/7+o+Ox49eoSTTJ8V108Sm5Cmh4gUsXfl7KlTdO3RI1N+Pz8/jJO00iefoNbDSsaFOHnwWK7q4el959HHhAR5FP1Gf5seHtt1jFL6mecHLoauXHx+WqOHWflf0sOGwO60Se/X3TP/AARBuCqKYkVBEGLJfD0EQPx7zNIfRdoiwxqAfPnyiVWrVv03ms0RFy9eRNOfz3Po0CEGDuxPl64VGDu+Me/eRTBqxGq8vKpmu28yNTWVdu1a8eLFQ1q1KUFwUAx799xh06atNGyodgBUpkxx2ncszLIlJ3n1egGQEYniyZMQ2rVeSodOFdiz6yYBARGYmOpTtqwbhw7eZ+3aDURGxhEdE039evUpW7Zs+hNPT09Pxo4bxb69+xFFkRYtmzPrj7npT2ZTU1NxcXGkW4+yPLjvx8HDI/jLwRLIqFt7HWPH+lKnTk3WrduEubk5X+NX+7xyimYC+v+UeSuXMWvyNA7fPYJUkGDhbM+CDavR0dH5euFPkEgk3H7zmB42tSiYR703K1Txkfmv9+OeWj5LfqlUipjNPVclqpDL5FnSv4c1y9bwavdbOpv0QyJISFYls3nan+Sxt8mR97C/+qkUs4aQUCF+l1OSf5d/PezAe+DTTWv2QBCg9Zl0gFBBEPKkDXzyAGH/Sk81aMiG2UtXMue3yfy57w4yAUztnZmzav136eHNV88Ykq8cRczVjnyC42OY+uA0XVOzLu5JpVJU2emMqEIm+7anlJ9j9bI1PN7mT3ODgUgECSmqZNZN3vpteiiTZdtPUaOH2fG/oIdnBUG4B1gCxQRBMAKif3Cb2SKKYsW0v4Y/o30N/7skJCTQsmVT6tZtQN16npw5/YQHD9STtgOHBuPhOpKJEyZjZWVFVFQUGzdt5PHjB8TFJRDw7gV37k9FS0s9zenYuTwtmnYlICAQpVLJo0fPOHmmP9Om7CMwMBI7uwz/AtevvSIqKp4nT94xa257wkKjmTRxD/v23sHQSB89PT1GjhyZbZ8tLS1Zv24T69dtyvZ9mUzGvPkL6dWzO336VsvyfslSrpQu40ZcbDJt2rTg7NmL//g6/qpoJqC5jK+vL5tXLsX35Quc3Nzp3GcgBQuqvdk+ffqUrauX8s7/DR4FPOnSZ9AX4769fv2aTauW8Prlcxxd3OjcZ3B6XZ+SmJjI9i0buXjqKNpa2tRr0Y5mLVoyc9F8UlJSUKlU372Crauri7nUgLwGGaa7ljomFDNyRZKNgwoHBweU5kpC4kKw0bMBQKlScif1Dr+3+B2VSsWBfQfZs3E/CkUStZvUoGuvzt/kQvrw9qN0MO6dHv5AS6JFVd26bF6+NccDrkbtmrJn8iba6zVIH2A9i/DBtahHrjhX+dGI4r8X4gA4DAxM29NUBohOG0iFAx6CILgAgUBb1M4m/irTBZiV9vfQj+ygIAgxpK2of5qMWuO0xX/5gmlQkyM99HuDR8Ec6uGaxbx59RwHZzc69xryeT38cyOXzxxCrqVNvaYdada8JTPmLcoVPbTU0qWgaYbpl7WeIaUt7JFkM6F0cHBAYSQQGBuGnWHaCrhKyYWoJ0xqNheVSsXBfQfZt3kfiqQkajaqQZeeXb5JDw9uO0pT/f7peiiXaFFOVo9Ny/7MsR427dCYLaO301inRboevop5gbuXm0YPs/LL66EoisMFQSgCvBdF8a9guVV/ZJufI80D8Gf5pH8aNGRi6rTJGBjGU7iIPQMGD0IURQb238TokdtZu74XBQs54u3tTVJSEpUrV6BsOWeqVc/LvDlnmDCpafrkE6BcOQ+cXSxZv349YWFhCIKAr28IffrVoF2bZaxY1Y0CBdR7QMeM3kGBArZs2z4AQRCIioonb7481Ks9i5WruhAQ4E/TZk3YtHEzJiYm33xebVq3ITwsnMWLZ/L7H23S9pCCUqnixLGHzJzdlho1CuHuMornz59ne5/7/4BmUJaLvHr1iskDutHA6APrmhWglWUsM4f15uHDh9y9e4e5o/vQ3imG9W3cqWv0nvH9O/HmzZts6/Lx8WHigM40NA1mfRt32tpHM2tELx48uJ8pn0qlYljfbsieHmVpQ0dmVLPA59ga5s6YCqhj1v0T86moqCgKOnoQnhrNx+RYopLjCE36iJu1I8rklCz5BUFg1opZHJce53jkcS58uMCGyA00HdCUAgUKMGvaHPZOOU3Z0KbUju3Mk1WB9GrfL0cB7UG9p0qVrEImyTz5NdMy40PohxyfV81aNXGr58nygJ2cCbrOjsDjXNV7xuRZU3Ncx89CFEFU5fz4GoIg7ABuAPkEQXgvCEIPQRD6CoLQNy3LcdRx5XxRBxLvr+6HmAoMBE4BL1Cbej1LKzMLqCUIgg9qr5Cf9xaTC4iiaCSKomHaXyPAFpiB+gnEz4h595/nq3o4qg/tHWNZ38aDuoaBjO/X+Yt6OGlIR5o6v2Fz3zx0LBTC7HHds9fDAV3RCdzNyp4mzGmrxdvLi5k7U72VITf00NPFjRBFHBGKOD4q4glKiMbd1h7VZ/Tw96Vz2RJ1hZ3+Fzjqf4M5Pnup16slBQoUYM5vczg+8wT14xvQTmyH32Z/+nbs+216mCQi/ZseGmuZEx4anuPzqlmrJgWb5mNb1AauRF7kSPQ+HlvdY+qcKTmu42eh0cNsz0GKes/lWkEQ9qb1/2eN9+4Bd9P+/v24+4VyOSK7z+uf1qnh12Dbn38y9bemCGnW7YIgMPW3FuzaeZOYmAReer/H1dWVyZPH065DCf7c3ocePauhSEohOTmzxbkoigT4h/LHrKkkKO7TqnVpqlWZQeHCDjRoWIx6tWejI+9Mn14bAZHBQ+qSkJBEj26rcXMeRttWSxBFuH7dB2cXC+7euULp0iVYsmQJFy9eRPxGV9z9+/fH1bUALZot4fJlb65c8aZVi8WYWxhSq5YnMpkUd488BAYG5tbl/OXQPAH9Rl69esWl86fR1talTv2GmTZBr1+2kBGVnClgq7bZLmBrzsQacpYtnktqajJTGrhja6r2lOjlYskwQWDjisVMn5c1DNaG5QsZWc2eQg7quvLbmTKxnoxFi+ewYtPO9Hw3btzAgQjalMkHgI6WjCE18jFw10VCQ0PT+5eamsqlixd5+ewJTq7u1KpTJ0cr256enswRI2jtXo2kRAUqlQobfX0u+J2iboWy2ZZxcXFh/7n93L59m9jYWEqWLJm+AfvKoVs0M+iTvspe3Kgil14f4sqVK1SpUiVTPcnJyZw7c5bX3j7k9SxAterVkMvlWDtZ8SE4DAudDD8Oz2IeU77R530cJCcnc+7sObyf+VKgcF5q1KjOmCnjCOwZyJMnT7C2tqZYsWK5Zm4WFhbGiaMniI+Np1rtahQoUCBX6k0nF3cWiaLY7ivvi8CAz7x3HPWA7O/pEUCNXOngNyAIgikwBPVThm1AKc0K+4/j1atXXDp3Gm0dPerUb/CNeuiRWQ8lX9DDlfMZ09gCT2f1XsoCjsZMbSVj7tJZrNiwOz3fjRs3cNYLol01tSWkjraUYc0d6bPsHKGhgzLr4aWLvHr2GEdXD2rVzrkeLkiKpXPhciQlKBBFFVb6+hx5cpVe5bPXHxcXF/acPpSuh0M+0cMbR2/QzaJ7uu6UNy/P0YCjn9XDs2fO8vqFL/kK50/XQxtnKyL9wjDTztDDV3GPqNDy2/Rw3NSxBPb6cXp4/MgJ4mMTqF6nqkYPfzxzUJsDL0dtCdIXcANG/Yt9AEAUxdyLE5R9/V/8vDT8ukRERLBy1Qpu3byGrZ0D/foOoFixYunvJyYqMDLSJSw4o4yBgQ4pKal067KOevXqYWdnx7Fjx7l9T71Y9vJlEHGxChYvOknLVqUxMlJbk0ydsg8dHSkPn/yBgYF660X/gTWpVnkGrm6WfPwYj66uHA+PfISEhuLzKpj+fTeiUom89luIiYk+d+++oXmTBVSu2I6YmER0dLV49OQQa9YsxMTEmmPHTmFsbJyjc5dIJBw6dIyFixbSo+sKklMS6Ne/JkOG1kUikRAWFs3DB28yXY//b2iegH4DK5cuYMmEbjhGHMXw7S5G9GjK+bNn0t8PeO2TPtj6C0cLIyJDg4iOCE0fbP1FESdzfF8+Izv8Xr9Kn3xm1GVIVHhIpjTvp48pbpvZXEsQBIra6uPj4wOoPWT1at+ae5sW4up/G9+9a+jWsikfPnz9iaGRkREterVjpe8RgpIj+SgmsMPvAuYlnL/oQEMqlVKuXDlq166NmZnaAsfX1xcb0SnLoMYm1ZmnDzJfh8jISDo1bsPlufuQnQ7l7MyddGnenpiYGMb8PpqjqXt5EvWQcEUYN6Ou8tL8MV17d8m2L5GRkbSo355Vo05xf30SK0cco3WjTsTExGBnZ0fdunXx8vLKtcHWpYuX6Fa/K76rvfm4PZwpnSax4I/cfQgnikKOj/8CgiBYCYIwG7gPpAJFRFGcqJl8/jhWLlnAotG9sPM7i96Tgwzv0iLnevghGz10/Lwe+r95mT75TK/LSp/oiL/p4fNHlHDNvNdcEAS8XLUz6WHvTq14tGsW+aIu4nd8Kd3bNM6xHjbt1pF59y8QkBjNh9Qk1j+7iVFhj+/SQweJQxbdccKJZ9noYfuG7Tj92zGS9sdwZPIBOjXtSExMDONmjuK8dBcvYh8QkRTKg9jL+Ns8oFufz+th8/rtWTnqJPfWK1gx4hitfqAeXrx4ifa1u3Nrrh8vV8UwqvU05v2+IFfq/guNHmahLtBOFMXzoiieA1qj9oT7ryMIQv60v8WzO35GnzT8fEJCQihSxJNdO9cSn/COyMgn1K5dnYOHDqbnadiwPqtWns9Ubs3q8+jpaZOUZMytW7eQSCQkJSmIikoAICIiDlc3K6pXL0jBfKPo33cDTRvPZ9nSUwwf2SB98glQqpQbhQrZU6qUO6/9FvLMex46unEoU+OZP/8ER4/cZ8WqbpiYqO9VJUu6Mm16KwIDP9KydRm8X81jzdruPHwynXz59Rk3/tsiK+no6DBu7Dju3HmIltyA0JAYnj17z+FD96hXez79BwzA0tLyO6/wr49mAppDfHx8eHppHws6eVAxvxl1ilmxvJs7qxdMJSkpCQALaxveRcRkKhcem4CuoQk6+kZExmUOBfA2LIY8dtkHJLeyzoN/eOa6PsQmIkrleHt78/z5c0JDQ3FwceXVh6Qs5V99SEwPCbBpzUpqmwv0LV+Q8u72dCuTn84eJiybNztH596pe1eGr5iGT75k7tp9oNHkbvy+YPY3D1Ds7e2JEIKzpEdIgnHxcM6UtnTOQqqo8tPEoTLFrPLR3LEKpRIdWbNkJQULFmT94TWYtdXFp9Ajig7Kx5ZDmz5ri79w9jKMA0viqVcXR0NPPPXqo+PnyZL5K76p/zkhOTmZOeNm09W8I2UtSlPMogidrdpzc9+NTO7B/ykiQo6P/whvUO+52oDapVxvQRBG/HX83K79/8PHx4fH5w4yt5kn5d0tqVUoD0taerJq7vSc6aHBt+mhpbUdfiFxmdI+RCsQBa3MeujkxqtgZZbyLwNTMvRw7UrqOyYxsJYbFQvkoVc1F3oU12H5gpxZRXbs1pWBC2fywEaLC/oKao3sy4x5c75LD0NVoVnSQ8RQnP+mh4tnL6JUfFHqWtXA07wgDaxqUSgyL6sXr6JgwYJsOrIa+05aBBW/T5kRHvx5+Et6uBSjwJIU0quLg6EnhfTqoeNX6Ifp4cxRc6kv7Ulho3LkN/Kink43Lu68rdHDH0uyKIrpP4S0p7Y5s+vOfYan/Z2fzTHvJ/VJw0+mY8f2pKQk0L5jedp3qEBISBQurmYMGTwApVL91f3999ls23qXN2/CWbXyLN27rmPenFOMHDWW174vWLuhI8nKLTRtVpzRI7eTnJxK0aKOvH0TRr8BtTh9bjwFC9nTqnUZLC2NUKmymkrItaS0aFUaa2sTHBzM2bNvCFFR8YwYUQ8jI10MDXUz5S9Q0A6lUsX0Ga3SNV8QBCZNacLOHTsz5Q0ODqZbt06YmBhhbm5K3369+fjxY5Y+mJubc/36LaTSfPTusYMli24zcuQ0ZkyfmVuX+5dEMwHNIZcvnKVGXhl+b3z4EBRA6Pu3fAjyp6idlEePHgHQqe8g5lx4RXiseiUmMk7B7LPedOw9kHY9BjD7pA9R8erBWVh0AvPP+dGpz+Bs2+vUdwhzzrwlLFpdV2hEDIPWniMk4A3dG1dhTu/GjOnSlOP7dnIzRMWNVyGIoohSpWLfXT908uRNj5F348I5ahd0ylR/WVdbnt+/k+Pz9/LyYurc35m1bD41a9VEIvn2r46DgwPOJW25H3cFlahEFEXexHkTYelPzb+Fh3lw/S7FrPJmSitlXYjr564A6pAMw8cOY/H6hXTt0QVDw8872Lt+8Q6O+pmfTjgbeHHpzI1vPoev8ezZM+xFW/RkGaIlCALFpEW4cPpCLrWS5vUxp8d/g9nAetTmZvrZHBpykcvnz1LdSRf/175qPXz3lvDAAIpYyr9BD1/lXA97DWXWwUDCotTu6oM/xNJ/4VVCA17To1kl5g2sz7iejTh+YCdXfeVcf/ZBrYdKFXsuB6NlXiBdD29eOkO9YpnjIZfPZ8WLh7dzfP5eXl5Mmf0HM5cspGatWt+th3bF7bj+8TrKND18Gf2SQON3WfTw/tV7FDbN7IiiuFkRrp29Cqj1cMS4YSzd8HU9vHbxLo76mQOdOxl4cenM9W8+h6/x7NkzzJMd0JFmWOkIgoBbSnHOn9Lo4Q9kbdp2BAAEQTBBvV/1X0cUxd5pf6tlc1T/GX3S8HOJjIzk+vVrXL81ldFjGtGtexXOXZiAlpaM+Pj4dF8ADg4OPHz4BANDUx49lODpWZ/Hj5+zdcsm1m3sTsWK+ZBIJKxc3YOkpBQcbAfSu+dm5HItqlSczsULz3FxteT0qScoFCksWnCC6LQxNcC1a6946R1MtWoZ2qqnp42ZmT4WVkbExyfj45PZyubg/juIoojf28x77HV1tUhKSk5/rVAoqF69MmYWUTx9MZM796fw6OEFnJxs0daW4+iYhy1btqTnt7GxYeGCRTx69JwLF67SsWPH/wEP5P8MzR7QHKISRd4HhVDPww5p2mBDkZKK7xt/Gqa58i9dugwp4/9g6uJ5KGI+omVgROfBE6lWQ731QxRFxqxbRnJCLPom5vQcPwcvL69s2ytZsiTdx85i2tJ5xEf74uP7mtblnPDxD2du55IIQFB0Cr6Kj5yXOHFBYcDKP+8iCBLKVqvNjCEZD33kWlokJqci183w0piiVCF8Y7Dy3GDu8lksmbuM/YdXoFKKFK3iybqpq7Lsv5JIpaSqlGhJMwZ2ycoUtLS/3SOjTEuGMiEVmZBRNlWVgpZ27oSF+RRdXV2SSM6SnkQSuvq62ZT4DkTg2+Le/b9HFMXpP7sP/yVUIrwLCqGOk1NmPfQLoPE36eFykhNi0Dc2p+eE2V/Uw6Th85i4Yi4JMYG88nlN24oO+PiHsaC9J4IA7z+m4BsXyhnBmdPv9Fl24g4goUzlusyYk+EyP10PZRl6kKJUIUj+fT2cvXQ2S+ctZf3RdaiUIoVLe7J68uoseiiVZaOHqmTk36GHci3pZ/Qw973d6urqkiJktdBJRoGuvkXuNKLRwyyIorhCEARtQRCKor5CL0VRXP4z+yQIgg5qh00V0/p0BVgliqLiiwU1/L/jypUrlCjpiotLxr51qVRCl66VGTVie6Z9lMbGxlhZWrFm9XpA7Wju1as3lCvnDqjvI48eBTBocB3atl5Go4Z9GDbUnQ4d2nDksNpJXeUq+Vm4uBOTJ+7B3WUYHTpWIiwsliOHb9O8RWl0ddXad/bsU7p0XImJqR5bNl5GFEVqVPudhYs64e5hzcEDd/lz61UWLWpBsybTeBe0ND2c1rIlp7Gzs8bY2BBDQwNKlCyFvb0Bc+a2BWD8uF2IwOlz43Bzs2b/vjv069cLExMTGjdu/MOv+a+IZgL6Fa5euczWNYt55f0MRVQIzYpbYm2sHmAFfUzBOzAx0+p3hYqVqFCxUrZ11alXnzr16ue4bT19A/SM9AkN88fcSOB1UBRdS9uhnfaF15enUs7ahN1HvJmx5wQGBgbZ1tOgVTu2HtpM/4qe6Ssqex++pmr9jC+9SqVix5Y/ObxjH8mKJAqX9mLgqGHpwXNzC21tbUZNHMGoiV+2iqzXshGnd9+moWMFQC0yJwJvUKpReQZ3H4jfy7foGxvQeUBX6jWo98W6mrWtz8llFyhkUCe9rpeJ52k1MPd/9EGBQdz2u419kA12BrZY5bFClME95UP6NRqUa+18o8O1//cIgrAPGCWK4htBEFagHuRMF0Vxz0/u2v8rrl65zJaVS3n1Ik0PC1tjlebkITgmiZeh8T9WDw3VemhhJPA6OIrulazRlqvbM9ASKO9mwM7bz5mx8/Rn9bB+iw5sOLOWIbXd0/Vw1413VKnbJD2PSqVix9atHNu9m5SkJAqVLEH/4SN+iB6OnDCSkROyjymX3udWDbi05Sq1rNVx40RR5NyHy5RtWZZB3Qal6aE+XQbmRA8bcGLZRQoa1E6v69UP1MNH729ikOiIta491nmsQEvEV+cOUxr3zrV2NHqYGUEQqgEbAX/Ukz1XQRC6pe0H/VlsAWKBpWmv2wFbgVY/rUcafgqGhobERGddmAoLjcbSyuqLOiuRSMif352rV1/h6GhOy2aLSEhMxsrKCC0tKStXLmPJkuWoVCoWLu5E/vzqeM0qlYrFSzuz7c8bmJqUolBBWzw8qrBwwXxKljqJgYEuQwZt5vCxkVStqn4ieuHCc+rXmc3ECbuRy6SUK+/BpauTCXpnjLmFAdUqz6RZixJcveLL+XOP6du/JsNH1CMiIo4WzRbTsZN6/BodncCaVed4+mIONjYmAPTsVY2w0GjGTxitmYBqyMrFC+fZuWQikxq7IK2cj2PXJTRf9Jj6xaxRqgTehiupXDgv8fHxud72kydPWDy1L1Pa2GJc14FAf4FJO9/hF6FPcUcTAKQSdYxNIx0ZiYmJnx1wtWjdhvm+rxh85AKFLA3xiYzHukBRpvTtn55nwcw5hJ9/wjCXmuhI5TzyeUO/9t3Ycmg3+vr/vgVjtz49mPrmLYtu7sNRywK/5DAci+flwr5zNNarQwPzasQmx7Jt8ibiY+No2fbz97CefbrxxncqN8+vxVC0I0bynvKNitK5a8dc7fPJ4yfZMHEN/T16su/VYYwSDUkKSybFQcn4OROwsbHJ1fY0ZMIjbfJZCnBH7XDjNKCZgOYSF8+fZ/v8KYypUQBpES9O3PWm1fo71PPMQ6pKwP9jMlV+oB4umdGbKV2tMNa1JjAghUnrg3n7QYcSrkbAX3qowlhP/mU9bNWG+b4v6bv1LIVt9XgZpsDKoziT+2Q4Nl04exbxt28zo4QXunI5d9+9Z2Dnzmzct++n6GH3vj2Y/Hoy665vxVZiw3tlMC6lXTm/7zwNdepS17Q6scmxbJ20mbjYOFp9VQ+ncOv8OgxFW2IkgVRoVOSH6OHKMRvoYt+fo3778UswJul1EnK3ZKYsGKfRwx/LfKC6KIpvAARBcEOthT/T6U8+URSLfvL6giAIj35abzT8NCpXrkxMTCrb/rxGh47qSZqfXzgLFpxg259fv2X36t2PVs2nYWysQ+8+1RkxSh3PPSjoIxXLT6NmzaoULebF5k1XqVe/CBPG7eLmDV9MTPQwMjJi0qRJyOVqC7gmjZvSunULgoKDadasZPrkE6BatYI0aOiFrq4WW7dljJeD3oG9vRn37vozf+4HihYtTtNmpZk1W/2008rKmOEj6rFj+3XGT2jCmzdhODpapE8+/6Jq9YIsX/Yz14R+Lj9lApq2H2Ed4Il6da478BLYBTgDfkBrURQ/puUfB/QAlMBgURRP/ai+JScns33rRi6c2o/3k6ds7FMUaxNdvAPiCYxMoZyzGTd9FPzepjKuVkb02/eUwoULo1KpuHzpEtfPn8HI1AwzmzxcPnGY6I8fKV62PF37fJs3q02rFzCioRmy5AgiohORy1XMbO/A4LV+NPeyAxFik1RoKyXEoIOFRfbmTNHR0Wxau4KHd68i6OshK1aFae3aY2trm54nJiaGGycvMKlgs/QnAsWs3QhSRHH08GFatWnDnl172bvpAMnJKdRqXI3ufboRExPDvp37CH0fQrnq5alVuxYy2ee/Urdv32btwg0Evw/B06sQ/Uf2wdHRMdu8MpmMGfNnERISQkBAAM7OzqxbtpZq8vI4GaodlRhqGdLapgnrVmyhRZuWn7WXl8lkzFk4g9DQUPz9/bGxseHkkeO0rdscmVxGo7bNaN2+LdJ/aJK8dsEa2ls1Q1+uz+hSQwiIe8+baD+U5aTUqJV7HvhFQKXSbN/+DA1Rx98LFgQh9au5fwF+eT3csolzxw7h/ewxa9uWxdpIj5fv4wiKVlDG3oJbAXHMaFUZVytjBhx48mP0cM18RrQyQqb8wIfwND3sZcPgRe9pWdoaEYhVqNDSlRCt1P2yHq5fwaP7V0DXAPLVZMqUrHp458xZFlarkq4ppRwdCIiN5djhw7Rs04Z9u/dwaNtekpOTqVa/Fl16dScmJoYDu/YSFhRMmSoVqZkDPdyweD0hgSEU8ipEn2F9v6iHMxfOzKKHVWQVcPxED1taNWXT8i20/Koe/p5FD9vUbY5MLqdx26a5ooer562loUF79GT6DPQcRVBiAP5xb9CpoNTo4Y9H+tfkE0AUxddpsUF/Jg8EQSgriuJNAEEQygDXfnKfNPwEZDIZBw8eoVmzxixeeBaZDLy9A3FwsOPkqePkz58fJyenLOVEUWTY8CFs+3MrJUq68uD+a4aPrJ+udba2pkyc1JR9e29z4/o9nj/TYfXKM6xe24MLlyby5k0YA/puYdjwwSxbuhKAUqVKUahQQQp5mmP/t8gTAA6O5uzfexulUoU0bQtESoqSu3fe8PTFHHZsv8GiBWdo16FZpnLtO5Rn3JidTJuyn85dK+LvH05kZBxmZhkLo7du+mJqapZr1/V/jZ+l2ouBk6Io5geKog7aPBY4J4qiB3Au7TWCIBRE7d2yEGrX4it+lJCKosiY4X0Q/baxvLcxzhZgIotl55kHzNn+lMJGtjRycsVaqsXi43cZtv8xnQeMREdHhzFDBnJx+VwqKkIxeXCRdVPGUtkgjjVNClAw+imDuqlDiOSUtz4v0FOGY6qTjKuNNkZ6MqRSJbHJKcw8+YoTT0O5EZjM+ONvGDF5ZraDjaSkJAb1aI9L1BXWtXdiXiMLoh8d5sj+3ZnyBQYG4qhrlqUONwNrXj9/xfQJv3Nk+kUqR7SiXnwXXqwNoUXdVvRs3J2oXSHkuWvC8WmH6N2hF8nJWfc/Apw7c45p3WeR17sczVP6onXBmt4tBnw1yK6NjQ2lS5fGysoKn2c+OBtmFiVtqTayJAkKxde3kVhbW1OqVCmmj51C2N7HDLJsQC+jGjxad46pYyZ9tfzXSIpRoC9XPx0RBAEnQwcq25XHz+fNV0p+IyKIKiHHx3+Es4Ig3AO6AfsFQTACon9yn3LKL6uHowf3I/n2IRbUcsbJQIaRmMjui3dZcOQuxcytaOLmhI1UzpKT9xh+8CGdB47I0MMVc6iUHITJowusnzqGSnrxrG5YiIIfnzGo6zfq4esX6BKGqV4SbrZyjPSlyKRKYpNSmX7AjxMPPnDdT8nYPYEMn/jHZ/VwcO+2uIun2DjEnEXddIl9tZcjB3dlyhcYGIiLsVGWOvKbmfHW25uZk6dza+URehpXZqhNPWKOvqBNwxb0b9EZTr+i4GsJlxdsp1/nHl/Uw1l9Z1IuxIs+Bp2xumvCgDb9vkkPXz31wckgqx5Kk6XfpodjphCy+wn9zRrSXb8mD9aczxU9TIxOQk+WoYd2ek6UtazMm1d+/7juTGj0MDvuCIKwURCE6mnHZiDnXgdzEUEQngiC8BgoA1wXBMFPEIS3wA2g8s/ok4afT9GiRfHxeUPr1j14/TqMCRObsmBxC2Tyt5QqVZxmzRpTtGhBmjZtQFyc2gP67t27uXjhGC995zBxcmMcHS2yOICztjFGqVTh6mZJTEwMnp72PH36njNnnuLubs2uvQPY9uc2IiIi0su8efOatu3KsX/vbRISMkyDExKS2LP7FjZ5TKhXZzY7tl9n2dLTvHgRyIhRDbCxMWHwkDqkpiZz6uTTTP3Q0pKhq6vL7VvRFMo/hpQUFc2bLuT161CUShUH9t9h2pT9TJw47Qde5V+bf30CmjYwrIzaYyWiKCaLohgFNAE2p2XbDDRN+78JsFMUxSRRFN8CvkDp72k7PDycq1evpnvYAnVMuOvXr/P06VOePHmCruIVXerYo6cjw0BfTpJSxZ4rQfxew4uqbnbktzFnUPki6MkNadZ3FA0aN+HKlSvoBfsxpFIxPO2syG+kxbqm5dh08QnXfQIp7mRJI2c9DuzZlW2/Pnz4kKVf0TGxxCamoqctQwBszHRRCVIUKjlRjhW5Y1CKSPe6dBg4+rOmTCdPHKNCnmTqedkjk0owN9RhfKN8XD6+J/0HDepwAAGJEYh/20jjExuMtaMdt088oKphY/Rk+sglWngZVsD/kT/N5PUoaVkMVyNnGlrXwthPj2OHj2Xbl6UzV1JftwMWOjYIgoCLQT68EquyfvnGnH585Cucj7cxfpnSFKlJKLVV6OjoZF/ob9y9exft90nUdiiNtlSOvlyX5k6VeXPjKQEBAV8tHxsby/Xr13n+/HmW66VrokdscuZQEW+i/cjnmT9HfdPw/YiiOBz15LOYKIqRoijGiKJY9Sd366v86nqoFeFPhzIe6GnL0dfVJkmpYv+9t/xezYsqbvbkszZnYLki6MkNaN7vEz0MecvQykXwtLOioJGc9c3KsvnyY677BuLlaEVDJ/1v08PoGOISUtHTlqr10FwHJTISlVpEWlfjJhUJt2tC+35jv6iHFd3jqF/OBplMgrmxNhM7OnHl9K4sevgmOibL7/tZRAQW9vY8uXCbNq5VMdDSQ0sqp7q9F0HPX9Pdpjzl7QuR19yeNm4VsQ0XOHbkaLZ9WTl7Oe2tWmCtZ4UgCOQzcaeKpAIbV2zI6cdH/iL58Yv1y5SmUCah1FZ+kx7K3ydT074M2lIt9OW6NHWswuvrz/6xHuqb6hKfGpspzT/+DfkL58tR3zT8I/oB91A7/emf9n+/n9SXhkAj1AtmLkAVoGra/w1+Up80/AJIJBK2bt3Ipq29GTGqPjVqeDJrdhsGD63Bu/dPWb+pAw0b2/H6tS9lypahU6eOBAaGs3nTZYoWdcTf/wNPnrxLr08URbZsukLNmp4kJiSxaGlnHj8OIDVVyeQJu6lTaxa6ulo4O1vj5+eHUqlk/fr1hIeHExOdSJWqBahS8Tc2bbzExg2XKFFsAl5eTly+Opm27cpxYP8d7t97C0DjJiUAtfMkC0sTTp96yprV50hKSiE4+CO9emygSJFinDx5lsREBWFhEUgkFhT1HIuuVhf6993M1Kkz6NChw0+59r8CP8ME1xUIBzameWi7BwwBrEVRDAZIM5/7axeyHXDzk/Lv09JyjCiKLF78Ow8eHqJYCRlv94oI5KV8mVoc2rWMMgXkRMSI3PFOolnJjDl5l8bujNz6lHxmxuhKJShSlITHpmLv5EJzLUNevHwBNOXG+bNUc1YPelJSUohPTmHp7QfYmafy6N0TNl1RUNzZkY9PHmbp15J5c7l75gRFLYzxj00ES1tmLV2Grq4+84+8Z1JLKe55dAkIT2L+kTBs7exZsGQFk8cOwf/xUXQ/nOLIlmScPKswbtLvmVaDXj19SBXHzO74JRKB/Da6BAQEULCg2tbd0NCQSo1qs/XkRZo7l0VPrs29EF8eEkZfVxdsRKdMTwPiU2MxwxQtZWavicUMPbl04iLNWmY2RVCpVCiiktDTybwny0U/L2fv7cjx59i5dxf6nOqJfrQ+LkZORCfHcPDDCbqN655jd9Uvnj7HTW6dJd1dKw++vr6fNYED2LFlGztXbiG/jj3RygRiTZQsXLcsfcN8n5F9WTVuOc3MGmCuY0ZA7HtOKS6wZNCyHJ9jzvhPhRPIEWlPAcsBtQVBEIGzwBpRFH9W7Luc8nP0cO5MHl49TglHPfaGJ4OpK+UqVefA1tWUdtAnMlHJvYAYGroYpZfrWKkw44/dIp+ZEToyKYqUVMLjU7B3cqGFdmY9rJ7m3TA1NZW45GQWXXtMHgN48MqbTRfvUtzNgahs9HDpvDncPXuCYtZG+MckojK3Y9aS5ejqGTB/dwCTOklwt9chIDSZebsjsbW1Z8HiFUwel6aHESc5sjUZp0JVGDdpZmY9fH6f6u6ZPVFLJAL57bWz6GGFevVYefUynQoXRl9Lixv+AVyPjaOXqyvuujaZ9CY6KR5ruSG6YubbaRlLD86dOk+zFs0zpatUKpJikjCwzryXNK+xKzseHszx59i5d2d6n+yFXoweLobORCdHcyTyBN3H9/gmPXSVZp2wu8q/rofbt2xnx7KtuMuciBHjSTBPZvGGpel62G90H5aMWEVt3WaYapsTmBDAVeEUK4YsyfE55gyNHv4dURRTgGVpx8/ui/+nr9O0LGcrJBr+XxMZGcm7d++pWzcjTJ5SqaJ6jUKsXH4GLy9nrKyM2Lc7hdu37gICHz4kMHH8Hl6+DGbegg7UrzObfgNq4epqya6dN3n3LoKKlfIhk0np0aMqu3bcoGxZd36b3opWLRbzx8xD+PmFkpCQgIeHC2FhYYwc3YBpU/ezfGU3atUuzNYtV3n65B1KpYrOXSqjrS2ne4+qdO9RlaSkFNau+si7gA9cv/aK5ORUPoTHcfr0OcaPH8XggVvR0dGiY6eOLN+tDnMrkUjQ19fn0sWrqFQqEhISPuuj4L/Ez5iAylBvhB8kiuItQRAWk2Ze9hmyu7Nk6/NOEITeQG8g043z+PEjfIw5yLI1edJvzGtXP2HH+ovs/KMkWmleFO8+CWH0olf0bKR271yxiBURjT1YvfENfpHJaOvoYmPvjI6ODpEJCozM1LbbxuYWRIT7qk9OJmPd/af0rWOGjbkEG1MDBAT6bXyFo1P5TP09feoUodfOs7B2ufR+nX31loV//I573nzUcTJk5dkAQj+GYG6kS6vq+dn9WJ+1qxbjqe9N+14u6XUtPXSFPbu206ZdhiMJR7d8eN+5iZdrxn4oURTxDVeQJ0+eTH0ZMnoEex13s/zPXSgSEilZsSwrh2wgISGBD5LgTHnlEi3ixFi0tDKHMYlJjsHEwiTL5yKRSJDqSUhKUaAtzbjvBCX641LGOUv+z2Fra8vSHStYPnspJ55ewNjMhJ6z+1KjZs73Ezm5OvNYeT5L+rvUCBwcHD5b7tmzZxxavpNR7q2RSdQ/G5+P/owfPJp1OzcBULN2TXT1dFm3cC0RoR9wyefKgjGLcHV1zXH/coqoGXD9nTmAA7ActWb0BdyAUT+zUzng39fDY0eJeX6aNV0LpuvO6lPebF9yla0DqqOV5mX7nk8g43feo2tlTwDKe9gRGVeEDaceEhCViLaOLraOTujo6BDxdz18+Up9clIZa25706ucHVZGWuQxNQAEBu17hJNL2Uz9PX3qJKE3z7GkUan0fp154afWQ4981Mmvy4qj7wmLjMTMWIfWdfOy64aRWg+NntO+WYY56tL9V9izeztt2n6ih6758X51meKfPIATRZHXwUlZ9HDQyJHsdXDgtx07SEpMpHi5ciybPYeEhAQ2J33IlFdHJicqJR753/QwShGPiZMpf0cikSDRkaBQJqEj1U5PD4h7j0txlyz5P4etrS3Ldi5n2eylnHlyAWMzY3qN7fPNevhAlTUeZ6Dyw1f18MCSXQyw7Zyuh77RfowbOIb1u9VWLTVr10R3pS5r5q/jQ2gErkVcWDJugUYP/wUEQXhDNlohimLOv2C5jCAIjVE7R7IFwgAn1NsNCv2sPmn4uagdugmEh8dgZWXM1i1XmDxxLwkJSSQkJDF61HZ8fUKoVrUjRkY6KBSpJCcrSUhIZdPGy0yZ2pyk5FRGj9iGIAhYWxujpSVj5fIz7D80HEEQ0NaWk5KiRCqV0KRpCQYP3EzRosVp1641DRoVJCTYikmTm1O2nAeTJ+7l7p03GBvrUrVaQVq2KsPv0w9QuUp+rK3Vpr3jxu7ExakOfXtvoErVAty+/Rp9fT1sbW05deo8qampao3/TGxoiUSimXym8TMmoO+B96Io3kp7vRf1gCtUEIQ8aav9eVAL1F/5P70T2gNB2VUsiuIaYA1AyZIl0wdlx45vZcTYzHscUxIUtK4iQUuW8SUp6WmDqfFrtp8JoG0NdZMSiYyPMl2idUwo4agepEQlJHLgdQg1iibTpm4tUhLi8A94h45UQkFbK5Arcc+jTVxKKnKp+klB09Jm3FNmBMAFOLJzO32L5M3Urxoezuw+doVxcxey8vcBTG9fFBszXSKik/htdwCd+kxgzaJpbByY+aFHl5p2DNu2I9MEtH7DxvTevp78eT5QzMWclFQVW676k79kVUxNMw+MBEGgVbs2tGrXJst1tStqycbjc1EoEpAIAnKZDsmGKbxM8aUMajOEJGUSF+KvMaPLLFJSUli2YAUn951GTBVxzudIg3Z1ObNyLzX0mqEr0yciKYwbklMsG7Qgu4/yszg7OzN35fxvKvMpFStWZJXBUu6GvqC4VT5EEa6EPELP3QIPD4/Plju0cz+1zLzSB1sAHqZOHH9zl/Dw8HSHKhUqVqBCxQrf3b+cogk7kIW6QBFRFJUAgiCcBx7x609A/3U9PL5vG2MrO2TSnaSkJFoUNEyffAKU8LDDRP8xu2+/omVJ9W9DIpMTJdMlWjezHh58HUKNYim0qVuL5Ph4At4FoC2VUMjOClEq4mahR2yKElmaHjYubMPDlMz7FI/u2kH/4m6Z+lUzvxM79l9h3JyFrJzdjxk9PLGx0CEiKolpm4Pp1GMKaxb/xsahmSeQXerYMmxT5glo/QZN6LNrHfkdIvHKa0pKqopNp0LIW6x69nrYti2t2rbNcl1N8tkx9eJ6EhUJSBDQ0dIhXkfkUcw7KhubAKBITeZwyH0mz1hASkoKKxct5/SBk6AEh7yO1G1dn70bD9PMsgH6cj3CEsM5lXCBBQMWZfdRfhZnZ2fm/VM9NFzKvbAXeFmq9fBq6EP0Pb6shwd3HKCyXplMeuhu7Mz5gOsaPfw1KPnJ//pAGyCXAlF/N9OBssBZURS90kLFtPvJfdLwL/Dw4UNevnxJwYIFKVy4cHq6jo4O7Tu0Z+jg7bRsXYKpk/exZ/8QSpZ0JSjoI3VrzyIyIo4unc05cXo0u3beZO3q8yQmKhEQ6N93I2fPPGHLn/2Ji1cwYexuFizqSK3ahZFIJDx9+o6bN3zYtmMAs/44zOKFJ+javQoBfhHExMhwcbFKjwFaq1ZhatUqjCiKTJ60hzu33lChogclS7vi7jKMwoUdCA6JQiIRmDC+MT5vFiCXy9Ly76N//94cOHDki47nNGTmX79SoiiGCILwThCEfKIovgRqAM/Tji7ArLS/h9KKHAa2C4KwAPXKmQdw+1vaTE5OQlcvs5+OlGQRHa2/rVAI4O7qxKOYIhydp/YOXqJcTfae6snsyRPY9eIWxjpavEtMxbN8Jd6fP8aiGkXR1ZLjExTC0L1ncLa3JzY1lY+pIEhk+H1MQK6tjaOTAw8DMy9IJicnoSPP/BEIgoBEEChevDi9xixkyvI5JES/RUvPmE69plO1WnVWL5iMTJq5Lh0tCal/c3hhaGjI/NV/snTuDBZceIIglVGjQQtG9/q2rSAqVQqFDT1w1XVFQMIHaRgPrR8RWTie1Q83YyIzIYwP9Js6gIIFCzJp1BQ+nEygrWF/pHIpQd7vOOp3hI7j2rJnww4SYxVYOlkwZ+qMLw5yfgRSqZQVW9eyeNZ8Zl7dhSARqFSnKnNHDPliuSSFAm1p1vu3tkROSkrKj+pu9oiaFf9sSP5r8gkgiqIoCMKvbn770/RQTzuzBVxyqgod3awrtu4uTjzXcaXH3ocAlKpUnb2nl6j18OUtjLW1eJ+YSqHylXh39hjzqhZHVy7ndVAwww+cxtnRlphkZboe+n+MR66tg6OjA0/j/q6HCnTlxpnSBEFAKqDWwxGLmbxyNgkx79HSM6FT9xlUrVaD1YumZK+HKVn1cN7y7Sxd8BvzDjxGkMipXq81o3v251tQKVMpbu5EER1bBOB9ahRnpP74OQpce3YQcy1DAlOi6D1uMAULFmTqmMkkXY1kqE1npBIpAaGB7N91lDbDOrBj8z6SohVY2FoyY/7Mn6OHf65l8R/zmXNtB0gkaXo4+Ivl1HpokiVdS9DS6OEvgCiKkZ+8jATmCYJwGfUk8GeRIopihCAIEkEQJKIoXhAEYfZP7I+GH0xcXBytWjXj+fMnlCrtxvVrr7CxsaNp0+ZUqFCB6tWrM3/eQnr27ErvnutZuaobJUuqLSRMTfUJCY7i6o2phAYZUKaMO2XKuJOaomTtmgskJSWTmqqkSFEnNm28zPCR9fEq7kyfXuvp3LUSH8Jj2bXrJsuWd+X9+0iWLj7Fg8d/YGNjwu5dN9m54wYVK+Wja6eVzPyjDdraaguW1FQlhw48IjomiSKFxmKTx4RBQ2oTGPgRP78wTEwNsLUzRS5XDy8EQWDsuIbYWg8iNjYWQ0PDz14PDZn5WVP1QcA2QRC0gDeonYdIgN2CIPQAAkgLTiyK4jNBEHajHpClAgM+HWjmhKpVmnL44Go6ds7Y+2dpp8vRQ/E0qS0ipFmqhEUqCIrSY+uaZVkeny/buIXQ0FDi4+NxdHSkXf3aLK9TMv2JgYetDb81rcEJhR4EGaFtaoKDZcYen20HXlOlfZNMdVar35DjJ/bToXhG3KGnQaHYu+dFIpFQtmx5ypY9mOV8CnqV4/Ljx1QpmhHG4MjNEMpVbZglr62tLX8sXPENVyszfn5+fHwRSQePjI3SeXEn4UMitZvUoeyssnz8+BFHR0fkcjkfP37k3tmHtDXqh0pU8fjjXV5EPOJjagzPnz3nwPmvx3hSKBTs33uASyevY2FjRscebSlQoMB3n8PfMTU1ZersGd9Upmbjumy/vhx304yHT+EJH0nUF7OY7/0raAZcf2etIAimn4QqMQHW/twu5Zh/VQ+r1G7EwTs76FwlwxrPxlSf44+CaFRFTH8CGRadQHCSnD8XLvm6HtarzeKaZdL10M02D1Mb1eJkijYEvkXHIg/25hk35p2nn1OlR59MdVat14ijZ/bQqXSG064n78Owc8/3iR4e4u8ULFaOyw8fUMUrI3j5keshlKuSNbi3ra0tf8xb9S2XKxN+fn7EvwmlV7H66WnuQORbFWUb1aPMzKx6+OjifQY7dEIlqrgd8pAHwc+IS4rixfPn7D2z76ttKhQKDuw9wNXT1zC3Nqdd9x+gh3O+TQ9rNanD5strcDXOMHv+kBhJkmGqRg9/EQRBsED9xFFEvW98WdrE72ctzEUJgmAAXEGtd2GoNUzD/1PGjR+DmXkSr17Pwdc3lLq1Z2FikkJU9C2GDt2EhYUdefPl5+rVq4gqkcJFMraKvHwZjK2tKXnz5iE0zcZHoUimSbOSbN92DW1tfRo3KUG79uVZuOAEQwZuRqlSERWVyNLFZ2nYqAi6OnK2/XmNFcvP0KpNmfQ4nJWr5Kd/3w0sW9GFosWcqFXjD0aOaoBEKmHhvJPksXUhJOQ+74OXoa+fsVjbr896Dh26l3Y/zPgZaWvLEQSB1FTN1/lb+CkTUFEUH5LZROQvst28Iori78Dv39te69YdGT78HPOCXlOytMBrX5EbV03IW6gaI5Zco04ptROiozdUjPtt1Wdtt62t1RNYhUKBriBmMlcDcLc0I/ZJEJNnLWXciF7UKSDHxkjKee9E9BxLUbVqtUz5W7Ruw4gL51lw7QElrUzwi03k5scEFq77smfYAUPHMbRPB574v6OgvZz7b5PxibFk8arcd3IXGBiIlSSr0x4rwZKANwHUb1AfM7OMOEZhYWGYSawQRZF9b7cgjZdRQl6RRGUiVzecZ4X1SvoP/Xw/k5KS6NKqF3KfPLholSXuXhSDT01g1Ny+1K5bO9fPL6dUrFiRc5VOs/rKYYrpuBKliuNe8htmrV2YY4cfuYWIxuTs74iiuEIQBO00Rz4i8FIUxeU/u1854V/Xw3YdGX7lAkGHfSntrItveBJX38nwKNeIMfvvUsvDkMhEFcdfxjJ+dtbFuL/4VA91BLLooZulGXHP3zN5zmImDu1DLWd9rA3kXHobg767V/Z6ePE88y4+plQeY95GJ3IjXMGCdZu+eD4DhoxjaN80PXSQc/9NCj5RVixe+WP00F47675Oey0T3vn5Uy8bPbTSMkcURTY+2Y0sXkIVw9IkCAmc+fMaK61X0m/Il/WwR5uemPtbU1ivFNEvohhzdhwD/+j/8/Ww2hk2X9xLIakH0ao4nggvmbN+vkYPfwEEQagKbEIdZ7M26gWrGT/ZKVsTIBEYCnQAjIHffmJ/NOQyZ8+eZd68eQQGBlK7dm02bdzEM+9ZyGRSevdcx5ixjejXvxagdjZUv+4czp8/SrPmRdm//w7Hjt4nf351TGYrKyPev48kISGJ5BQ5LZot5NTJx4iiiI6OHEcnc6ZM2kut2oUZP6EJvftUY8rk/ejqRPD06QuWr+yGTCbl4IG7/Ln1aqYN0TY2Jowc3ZBihcehUomYmukzsP9GEhKSadSoBU2bNmXd+lmZJp8A1WsU4tJFb8LDolFbtqvZuvkqRYt6ZtnKoeHL/CeMlbW0tFiyZCu3b9/m2bN75HV1pl+vWmhpafHixQtuXLuEsbsZa/rXxcjI6Kv1aWtrk6qlQ3SiAmNdHVSiiri4OC75BOCavyT58uVj/a4TnDl9iuAPYXRuWgFPT88sN2YtLS0Wr13P3bt3eXz/HvkcHOlfsyba2trZtvuXy3tBEFi8Zjv37t3F/60PZVt7MrJixc8OFP8JefPmxV/pjyiKmfrvhz/VS2QdHzs4OBBOMAHxb0iKV9BQR72HKo4YWlh24cjmXbTt3CbTIO1Tjhw+iszHGi+DmgCYaFliqXRg/rRl1Kxd86vn+OrVK7y9vXFwcKBYsWLfNBhKSkri2rVrKBQKypYtm6mPgiAwddZ0nj9/zvVLV7G3NGdE3To/aTO5gChqAq9/Stp+oo2AP+oxqasgCN1EUTz3c3v266GlpcWSNZvVevj4Pq6lnOhV8xM9vHoJY1Mz1k7PuR4qtXSISVRgpKuDqFLr4WXfAFwLFCdfvnxs2HuMM6dPER4eRpfOX9DDNWl6+OAeHvYO9K1ZK2d6uPoTPWzxY/VwcXxIFj18kRhCS69WWfI7ODgQlBLG62h/FLEKOlg2BSBGiKW7XRu2bTtCm06f18Ojh49i6m9JJbPqAJjrWGCrtGfx9KU/Xw9n/5amh9fwsDBjQr0ZGj38dZgD1BBF8bUgCPdR75E/B5z5WR0SRTFeEAQnwEMUxc2CIOgBPySGsYZ/l4iICLr36M6xo8dQKtWrQa9e+ZCcnERKciohIVG8eB5Ir97V08sEBHzgwf236ieWS84ikYiMHb2L1FQVLVuV4emTdwiCQJ+e6yhfvi1FizmxeWs/JBKBxQtPsnzZaRo28iK/xwjs7CwIDo7EwcGc0NBYbG2t2Ln9Br371qB9hwokJacyfOhWho9sgIODOQB16xbht6n7GTy0Ds2al6J0aTeePw+kRtVZtG/fnkcP35KSkor8k21yN6774OcXzseP8dSvs4y69Yty6+Zrzp55wdmzWZ25afgy/4kJKJBmwlWWsmUze14sUKDAN5szCYJAr6EjmTZnOt08HZDHx/LyQyQbH73B+E04x4oWo0HjJjRt1vyrdUkkEkqXLk3p0l8O5Xfx/HmWz/iNCtbq1fQ1f/zOoMlT6dHr2/YvfSuWlpZUbFyRQwcPU9W4CtpSbW5/vI3KXZXlWgLo6enRtndLVk5bSzEqIIoiiaoElFopmJqY4hjnxpMnT6hSpUq27V09exNHWWaneNpSHbQVxgQHB2Nnl33EidTUVEb2H0no/VDslY6Ey8JQOShZvnl5jmzyHz9+zIR+Y8iHIzposTplKR2HdqNV+9aZ8hUsWDA9XMPPRLPnKQvzgeqiKL4BEATBDdiD2sOshr+R63o4bATTZ02nSwFn5PGxvIqIZNOT15j4h3CsSFEaNMl9PVzxx1SqOBghirBuTjQDJvz2r+hh6XpV2XT2Ak0cS6Ij0+LC+yfE2Wh/Vg9b9GjNhpmrqaRVAlFUEZ+aSLI0FVsTUzxSnL6ohzfO3SSfTma90ZHqYJhg9FU9HD1wBBFPgnCT5OG4GIHCRmDpxpU51sNJA0ZTQGKHjiBnjWIJHYZ0p1U7jR7+j6AtiuLrtP8FURQT00z8fxqCIPRC7ZXbDLWHcjtgFZ+x9NDwv4Gfnx8VK5YnNDQMpRLUu0cgOVmJIEjp2WMdW7f1y7L41a3LaqKiFKhU6vzqsiJTJu1j5oxDSCQSYmMVHDhwl0qV2jN5Ssb9Y9yEJly79gpHJwvkcjl6elr4vFmAjY0JHz/G07b1csaO3U1oWCx6enJm/x97Zx3e5NXG4ftNmtRdqUKB4u7ubkOGO8NhuLPhMGS4u9twGz5guLu7FeqeNna+P1KlpRQotq/3deVqcnLsfdP8cuQ5z/PHbho1KkbhgsP56aeiRESo2P/3dezszHn9Kph2rRfg7mHPth19adWmNOfOn6NgwcJ07byCP6Y0xc7Ogs2bzrJ61b/cujuF5088sbAqyZXLT7l//y2jR4+jYMGCX+V+/5f4z05A3w2K/TlotVoiIiKwsrKKX3GuVKUK5paW9GrVFC8LJQW9MrG0UwOsTIzpM3saxUqWio+HFhVl8H5rZmb2Se2HhYUxb/xYplQsiYWx4Tfkp5gYBo0dTZFixb74qvOg3waxt+BetqzaQrQqmupdq9OqXav3rr536NqeNwG+nJ1+HQ+5F5Z2Frg5eCHJJMJkwfEeElPC1cOFJ5oAHEwSBlZCCKJEKNbW1u8tt37NenQX9TR3SHCqd/nFZWZMnMHvk35P9fp0Oh2/9R5OB7sG2JnYAFBBX4L5M1dRomzJVGPhfRME6XrmSZKkmsAsDKvRS4UQf7zzfgMMziv0GM7s9BVCnJQkyQQ4ARhj0JItQohRsWXGYTC50mPw4NpeCJGit9Z0Qh43+QSIXfnPWF2P5WvpYc+WzfAyN6aAhyuL2jTCytSYAbP+pFip9NXD+ZNGM7tuASxMDHrYuKCaPhN+p0ixv7+4Hg4YMYS9Bfawfu1mVCHRVP25JgPatn6vHrbv3AFfvzfcWnyBzMIDC1tLvBxckckkgkRoqnro4uFC4PkAXMxc49OEEITrv8jezQABAABJREFUw1LVww1r1qO8GU1nz5/i086+vc7MP6bz24RRqV6fTqdj1K9D6epcGztTQxtVdMWYOXMFJcpk6OEPoodCkiRTIYQKUEiSNBh49KFCX5ieQHHgHIAQ4kGi+MYZ/KD8/vtwatXJxeaNIYSFJXVAJgT8e+IeY0fvxNHRkuXLjtGlaxUiIqI5d/Yh+iQG4RIgQ2lshE+OTNy76wsI5HIZlpbJw8YWKZKF8WN34OlpD+gpXWIUq9Z2p1y5nMxb0JbSJcbh+9qGXTu3MX9BB5r8XIInT/zYtfMyG9afpnKVPGz661eMjOTo9Xq6dl7GsCGbcHO3IyQ4lI0bt9B/QB98sg5Co9GSNasX3lldyZzZkZfPJNp3qEDjJsXxyTqYmjVrfsE7/N/lP2u38uThPWZMGf9Zh4KFEKxcsoTmNaozoHlTmtWszq7t2+Pf1+l0NCyeh6W//ESvaiVwtDTHWGFELW8Hjh4+hK+vL7/+0pZujWvQrXEN+nTpgJ+fXyotpsypU6co62wXP/kEsDA2pqyLA6dPn/7k60srkiRRt35dVm5dycZ9G+nYpeN7zeLi6DegH9qcEZh5mODo7IRMLudh+F1k7rpUd1iat/2ZB6b/EqkNBQyfwa2oUxSrXDDVgeXeTXspY5PU3X9Bm4KcOXrmg9d369YtXHX28ZNPAIXMiFIm+Ti4d/8Hy38LhD7tj9SInaTNA2oBuYEWkiS9u6VxBCgghCgIdASWxqbHYNh1LAAUBGpKkhS3DTRVCJE/tsweIPVVgM/ngiRJKyRJqhz7WAVc+MJt/jA8eXCfGX9MTBc9bFa9Ov2bNqNpjRrJ9PCnIvlY1LYxPSqVMuihkRE1vJw5evgwvr6+9O7Ujq4Na9G1YS36dP50PazoYRU/+QSwNFFSwdP66+lhvXos2bSGtbv+on3nTmnSw3AvNcaZzHBydkQul3E7+AFaZylVPWza5mfOS6cJUyfo4fnQ0xSqmLoe7tuym8ouSXeRizvl5dw/H74/t27dwl3YxU8+ARRyI8pa5M7QQwM/gh6OxBCiCeAMhklxhy/c5oeIEULEu6aWJMmI98QwzuDHYf/+A3TsVB61Wkvyj1NQtmw5vLwq4ONTiEEDN9Cg3jTGjd2ORpOy7zwzMyXnL47jp4ZFKVasCD16/EpYWHSSRVQhBLt2XmLw0HrcffAnV2/8wbyFHfm50SxCQiLJnNmRkJBw5s1dgEqloXIVg1VdlixO9Olbkwf33zBlWkuMYv0WyGQyxo5vwrq1p1i75gx1atfF0tKSJYuXExQUQmBgEJcv38BIbkWLZvMJC1exY/sFqlWZSpOfm5ItW7YvcWv/8/xnd0CzOJhi8fIEc6ZDv8EjP6mOzevXc3/3DmZWKoNCLidKrWHygnnYOThQtly595aTJMMB64HdO9KnqC35yhUC4MpTfwZ068iqLbs++nxSSuu7Eum7s5GemJiYMHftbH7vN5qQ5+HoJR2ZC3oy589ZqZ5D8vT0ZPzCEUwYNhVdiIxooihbvzgjxg5Jtb13z2R9LCkXlb7b+5uOvSoOPExkuroRw0r97fi2hIhIlN88rnlhuDlx7yliH3HvhaVU5gvSHegKxNlgngAWfOE2fxi8bMxR3j3HnGlT6Dd0+CfVsXn9eu7t3MWfZSugkMtRqdVMnT8/jXqoY2DXTvTM50TeIoaJ0dUXbxnQtROrtu78v9DDmavmMHrA70S8DkUn9HjkzczMqbM/qIej5o1k8ogpECZDpY+kZPUSDB0zNPUGhXiPpqWRFMpKGXpoaOsH0EMhxJ5Ezzt/ybY+guOSJA0HTCVJqoZBq3d/4z5l8JlYWlqgVCooWSobp089RK3WE6vGmJmZMGLEcKpVMzgeyp49M3nyevDn1Lh/Tz0Jx4AFxsYymrcohSRJDB5al8Y/LWTSpEksWbKYXzouY/DQ2shkMv6YuAt//zBG/vZTfD9q1SpAxUq52PLXeaytTbG0NKH3rz3JmjUzs2cf4MK5R/j7heGSyQaVSo21dYIFjlar48Txu6hUap48ecOECWPIkiULWbJkQaFQoFAYQrQcOXKc2XNm4/s6nC1brtKr53DatGnzpW/xf5b/7AQUoG05L9ot/xt138EolR9//GHHurVMKFEIhVxOeFgYwYFB1LG3YuKwYWw5eJAiRYow3T+K4EgVtuaGGJExGi1/Pw6gRV0b8tvoyefhQHBkNJtO3+X4rRc88YugRqkSVKpRkzadu+Dh4fHe9p8/f87qRYu4de0qLx7cp4a3F3ZWhvM7ETFqTr4NpFOZLx/k+1PJkiULa3asIiwsDCMjow+a3On1erZv3cbeTTuxt1FSrmVlWrRqmSZTvTrN6nB67ikqOyQcJ7kecp0SlUt8sGyePHl4JQsgODoUWxPDqr9Wr+Ws6joz66a/J8104eNMzhwkSbqY6PViIcTi2OduwItE770Ekt00SZIaApMAJ6BOonQ5cAlDNIp5Qohzid6bALQFQoGkLk/TGSGEBpgb+8ggBVoW8aHrzv307D/w0/Rw7TrGFi5q0MNwgx7WtLJlwtBhbD0Uq4dBEQRHqbA1i9VDrZYDz/xoYWNDXkvI6+ZIcFQ0f527xfG7z3gWEE71kiWpVKMGbbukQQ8XL+T2tau8fHSX2rncsLcy7AJGRKs59iKMtt+5Hq7atuaj9fDvv7ZjbaOkbPPKtGjVKk16WKtJPf5ZcYba7gkLA5f8b1GsQvJzqu+SJ08eXhBEUHQYdiYGJ1RavZaTkbeYXjf1+KDfjAw9/BEYCnQCbmBYLNxHwu5xBj8obdt1YOTwraxe2502rRZw7uxDdDqBEBJTpkyhWrVqPHnyhGnTphEUFM7G9WcwNlZgYWFCYGAEOsPhTywsjMmc2YGx45oAoFZrMTIyQi6Xkz27D/fu56dG1enExMSg1Wpo07Z0/A5mHJlcbdmx/SLnzj0kW3Znrlw5gbGxDcuXHqNvv5q4u9vx74l7GBsbMX7sdrJ4OwGCjRvOoNXoGDWmMXfvvGbf3ouULVuKR4+eYmKSYP5rYWHB8GHDOXbsGIcOHftat/g/y396AiqTSdiaGREZGflJAy5tTAzmSiUB/v6ogkOwNzHD3taeiDNX6Nm+A4vXrWXQuD8YNHIwZTNZYiKXcexlCC2690Or1eJuYUSYSk3/lUcp5mKFQi2YULYw1ibm+D69y4D2bZm6fCVeXl7J2n7y5AlDfulI+xzetCtZkM0KaL16M02LFUKpVHLybRA9f/v9G3kd/DjS4kkTYNTgkajOv6K5S3EkucTRtacZdfUWU+fN+GDZlm1acun0JTZd3Yi71gN/hT9aNw3zRnw4EodcLmfs7ImM7DGUnHhhrFdwQ/eQ5r+2/f7OOxEXduCjBlwBQoiUwnxAyptJyVbnhRDbge2SJJXHcP6pamy6DigYG3dzuyRJeYUQN2PfGwGMkCRpGNALSP3w2WcgSdJjUrgWIUSWFLL/XyKTSdiaKD9DD6Mxi9XDqOAQ7EzMsLd1IOLiRXq268Di9WsZNH4Sw4YPobSTNcZyGSd8g2jZsw9arRZXMwVhqhgGrjtIEQdbFGoYU6wE1qZmvHnwkP7t2jFtxYr36uHQLh34Ja8nnSvmYaOZjqbzd9KiTD6USmOOvQilx7DR/yk9HD1kBNorz2jnUQCZJHHgr+OMunaLqXNnfrBsizYtGXT2Iktv7iCrLBOvCSLKSTBn2IeNAuRyOWNmTeK3noPJI3fHBAVXY57SrGeGHsYnfOd6+D0SGwJmCT9OfOYM0sCwocNp3/42xQqPplTpHNjb2+Dm5snOnXt48+YNo0ePZsqUKajVGnQ6PUFBoFTK+GtbHwoXzsLsWfsZO3ob9eoVYuWa7sjlMnQ6PZMm7KFZ85aAQZNmTJ/FjOmz+H3U7zx7fox9e68x8Q81JrFHMaKiYti44QzFinlTv34Rjh27TXYfc47/c4EsWRyZN+cQQgisrExxc7Nl5YrjNPm5BBER0dy+9YolyzrTtJlhgW7TxjP0/XUN27Zto2XLlt/q1v7n+eAENPZgfV2gHOCKIY7TTWCvEOLWl+3e5xESGUOYVomNjc0nlXf28OCxvz8iKAh3CyskJM699qV05uyYRwuOHjlCjZo1WbXzb06ePIlarWZ+mTLY2try+PFjpq2KJCr6AbWzOXHigR9DShbCycwUuUKJMiyazrmzsXzuHMZMnZas7SWzZ9Etjw95XV0AaFOiKIU9Pfjz5gN6DOhBx3LlknkzFEJw+fJlrl2+hKu7B5WrVPmkgea34MmTJzw9c5veWX+KT2voVZ5F1/dw586dZOek1Go1/xz9h5fPnpOvUAGKFSvG7KWzuXPnDvfu3cPDw4PChQunat4mhODq1atcPn8ZFzcX1uzdwMWLF1GpVPQtNQoHB4cvdbmfh5DS0+vjSyDxtpM78F7nGEKIE5IkZZUkyUEIEZAoPUSSpGMY3P3ffKfYemAvX3bAlXhAaQ40A0zTu5EfWg+joglH/sl66OThyeMAf0RQMO4WhknUBV9fSnv5YBYNRw8foUatmqzavS9eDxck0sOpy8JRqe5T08uVf5+8YWCRojiZmiFXKFBGqujkk4Nlc+cydurUZG0vnTOTXgW9yeduiD3arnRhimZ2Z9KlJ/To34Nlqejh9SsXyeTuSeXKP5YePj9/g4F5asWnNc1Wijm3DqWih0d59ewFeWP1cObiOfF6WPsj9PDKhUu4uGZi9Z5N8XrYs1SpDD1MqenvVw+/OyRJqothsu6FYewZazUv0rYik8F3iVKpZP36zdy/f5/r168z6vds5M6dmwYNGnDixAmio2PQ6wUGlzNxHnL19OqxkivXJjFkaH1evQpi1Yp/qVhuEoWLeHHo4E1kMhMkblGlSiUaNmzEkCH96dipK5m9MnP5cjRFi3lTueIEevWujhCCKX/sIW8eNxQKORcvPGbY8AZERsZw49oL8hf0ZMPG3mi1OqpX/YObN15w98GfuLjYAHDt2jOqVJxA1Wp5sbOz4OemJfi11youX76cMQH9gqQ6AZUkaTRQDziGwXOZH2AC+AB/xA7GBgghrn/Zbn48UTFaBm1+QNdBEz/5bGCvocMY1KE99W2tMJUpue7nz7aHzxhZvSGvw0K4efEyNWrWxMzMjOrVkwYF9/b2xqNgWf7aupap1XOx4+ornM1NQSZHLpdhJIM8zg4sO3U5xbYf3blDnwpJLX/yZHLG/PYDateunSy/RqNhUM8eGL99RVFHW24eiWLF7JnMWLYCV1fXZPm/N27fvk0OZaZk6T5yF27fvp1kwPX27Vt6tu5CNo0DmeR2rFz7DyuymDNr6bw0h5HQ6XT069aP0KvBZCMrN7jKYrNFzF07L1UzwO+G9BtwXQCyS5KUBXgFNAeSKK4kSdmAR0IIIUlSYUAJBEqS5AhoYgdbphh2ASbHlskuhHgQW0V94G56dTglhBBBiV4GAdMkSTqBYcCTLvzQeqjW8Nuhq3QbPvaT9bD3sKEMbN+eOla2mMiNuOHvz84nTxhRtRG+4aHcvHSZGrXer4eeRcqwdfM6JpQtyK7bz3AyM0OSyZDL5cgliVyOjqy4cD7Fth/dvUPe6kkj6uRxdcLiypP36uHg3t0wD3tKSXcLHpyLYdW86fy5aNUPo4e5TZ2TpecydkxRD39t14k8cis8TKzYuPVvVrnZMWPR/I/Sw4E9+qK685a8Ju6c0Z5l6fT5zFq1MEMPf0A9lCRpOSnv5gIghPgWDolmAo2AG+J7PUycwSfj4+ODj48PAOPHj+f48ROoVCoMBgTvOqSXuHXzJWq1FqXSiAIFvChUOJIB/Qdz7NgxXrw4jk4nuHv3MQqFjJ9++olBQ8szdfIMihWrzPVrr/i1b0WqVc/H5k1nefEiiCdP/PD3D8Xc3ISbd6ZgbGw4t9myVWny5BzMs2cBLF92jBcvAunZq1r85BOgQAEvKlXOw769V2ndpix6vUCt0ZE/f/6vcu/+X/nQDugFIcTo97w3PdaF9vdnkwOE6U0YPn0ltra2PHv2DA8Pj492dJEzZ07GL1xEz5+bcc4vjCz2zoyu2RhbM3POvHqKm3fpVMsPHTWOV699uer3EOQyInUCG2MFCNDoBa/CwnHKlPJgyMHJmdehYbjZJHgiDI5SYWKRcgy3bX/9hWdYAG3LJGwEFX39hqmjf2fG4u/vmIWfnx/R0dF4eHggSRKurq7s04cmy+crQin/zoBx8m/jqassRE4Xg3VlCfKy79kpNq/fSOv2bdPU/q7tu9BeVtPUuXF82vPwF4wdNIYlG7/O/dLpdLx48QIrK6v3BqJ/H/p0GnAJIbSSJPUCDmD4lVguhLglSVK32PcXAo2BtpIkaTDs+DWLHXxlAlbFnnuSAZsTOb/4Q5KkHBi8DDwDuqVLh1NBkiQHoCSGX7yzwFxJkmSxpl/pwQ+rhxGSkpFzl362Hk5YtIgePzfnYmAoWeycGVXjZ2xNzTn7+iluWVI/bz109FhevX7N9cAXSDKDHtoaK0EItHo9r8PDccqUfBEKDHr4KiQcd9uEzZLgyNT0cDPZ9K/oVCvBgWmJ5wFMGzuS6QuXf9R1fw1S0sOD2vBk+V5pwynxjh5OHT2OZvY+5HM2TBTLkoOt9y+xef0GWrdvl6b2d+3YheJuGK2yJoQTyBfymvFDR7Fo3de5Xxl6mK7sSfR8GjDwndffghfAzYzJ53+fJUuWoFLFpJrH2FiBkZEMvV7P+nXn6fzLQBo0aED//v1jyxp2TDUagUaj49yZh+z9uz8+WQexa9dexowZyZkzhgXLggXzI5P5UaRoVsqVz4GxsYKoqBhu3XqJk5M1tesU5OCB6yxacISGjYoiN3p3QgxGRgbTX4CFC45gamJCq1at0vvWZJCIVCegQoi9H3jfD8MuwHeHo5Mzc2aNQR31CAsLGa/fKuk/aArFiqUe4PxdChQoQIU6tXF57k8dn9zIJBkPAt5yIsSPFfXqpVpWkiSGjRrLwA4tqV7Yh3mXb9OnWD5Uah16pQmLrtyix8QpKZZt0707c38fwdDSxbA2NSEyRs3cC1do+Wv/FPP/s3c3/XJkTZKW19WF+X8fR6fTIZd/HyER/fz8GNxjCKFPIjCWjFFZRDJ21mgKFixIpANc9LtDEcecAFwPeIifZTQlSiQMbIUQPLh+l6bZWyept7xLIVbv+DvNE9AD2w9Q2jrp/4KnpQf7Hh4gOjo6ycHzL8Gxf/5h9tjJOMssCNVE4ZzrI48qpteUChBC7MPgECJx2sJEzycTu5L/Tp7rQKH31Nk4pfQvhSRJFYGVwCmgOgavlePTcfL5Y+uhszNzpoxDHfgMC2M5ryNl9P990qfr4bMAamfLk6CHYW9YWT8Nejh6LAPataJK/lwsunGdXoUKodJo0RubsOj6dXpO/iPFsq279mDWmGGMrFAIazMTImLUzDx9gxY9B6aY//j+XQwv6Z4kLb+nA9NPXP7u9HBor8FEPQ9FKTcmwkTFqOljKViwIGE2cs77PqCYi8HF/9W3j3ltpk2mh49u3KZr8aT3vrpXbubs2pvmCejhHfuo65J0td/bxpWN905+ZT00J1Srwjlnhh5+DkKIbXHPJUka+e7rr9mXRAwG9kmSdBxDyBoAhBDTv1F/MvhCxMTEEHs6OzZFh2E9R4pN01OipA/btl5g+bKTCL0lzZo148WLF/j6+pJ0897gfXvVyhPs2X2FkJBwmjdvwezZs9iypQqSJDFh4jhKlnbCxEROgH84C+YfYtRvW/H0tOfVq2CMTYwoWiwLKpUGjVbHgnmHCPAPo0+/Wnh7O3H/vi97dl9GoZCzeOFRHj4M4ODBI9/N78R/lTQ5IZIkqSgwguS2+9/t/vTrV89pXtuWYoUMq8VBwWr6je7N7Pl7Ug38nRKjJv/BnGl/0n//IWQI3LJ68+fyJcnOHKWEm5sbw/+cy5xJ47ivekGTHUdxtLXDyTMznYf9RrFixVIsV7JUKaKGDGf0rJloIyOQmZjSqmtPatSqlWJ+hVJJjCZpjD8hBDo+LzxJeiKE4NcOfSn0thSZLQ2T5VB1CEM7D2fT4Q3MWr6AqWMmse/0OiQkchcvwNzRi5Lt1AhAL/TIpIR0tU7zwVh8iVEqlWj06iRphvul++idoY/l6dOnzBs5iUE5a2KhNBxTPPf6ftorEB/tdOP/gSlAFSHEI0mSLmM4e3UEOJTeDf2QevjyOc2beVOshsEcMygimn6/92P26h0frYejJ09izrQ/Gbh/PzLALWsWpn+EHo6YMYfZE8dzX62mxd/7DXro5UWXkSNS18OBIxk+ezq6qEgkYxNademdqh5GvxNnTgiBTs93pYf9Ov1KBVVhsrkZHC8FR4cysttQ1h3YxMxlC5k2diI7zhhireYuVpA5oxYn0yc9yfUwRqtFafIRemisJEaVwu+H0H81PRycq3oiPXzwgVKJyNDDD+EvSdLPwC6gPOk6Xf8oJmAIU2OCwWw5g/8otWvXZtWqNej1if/Vkurx+XOPePggkOEjRtOhfQeMjY05ffo00dHRJDfZBX//cPz9DVGOXr16RZs2bVixYgVNmzbl8aMHNGzsydWrz5g37xDOTlacPDMKKytTRv++lW1bzzNm1Db0ej0KhRHzFnTg1Kn7FCs8gsqV83D48C0GDBiMvb0DDX9yp169eh81nszg00irF9x1wCAM7rO/lXh9FJKkplgh2/jXdrZKGtc2Yt/e7bRr3yXN9Vw4f54Vc+cR+OYtWXP70LlvX3LmzPlRfcmXLx+L12+Oj1WZ1piVlatWpXLVqmnKX7dZCzYsmEX/0kXj8x579JR8xUsmG0Co1WpWLF7KP3v+RghBpbq16Ni18yc56FCr1SxftJxD2w+iF4Kq9avyS49fUvzy3rt3D9kbRfzkE8BaaUOOyHzs37ufpi2aMn76H/Gx5lK6ZkmSKF6pNGcv3qR07Iq9EIJ9vmdpMLRFmvvdqG0jlg1YQlPTxvEDt5sht8hTPM8Xc1Ry+NBhVs9bxe0b1/nZKR/KRCJbwtUnzfUY1hUzBlzvYCyEeBT7XBJCqCRJ+lKDnB9PD/UaimVLmGjaWZjQOL81+3bvoF3HtIcJvHD+PMtnzyfgrR/eOXPQpX+fT9LDJRs2fVE9rN2kJatXTWVYrdzxeY/cfkXeYqVT1MOVSxdz/O+9CAEVatWmQ+eun6yHKxcv59Cu/SD0VK5bnY7dOr9XD42D5GRzSfD6a2tiTSFZTvbv20/T5k0Z9+fkD+phsQplOHH3PhW9DJ+DEIKtj69S/9e0H/Nr0Opn1o+cyy8WNeP18LLfA3IUyftF9XDN/JXcvnmdnx3yYZxoOFLCNXua68nQww/SD1gGrAAeAWkfAKUvdkKI6h/OlsGPgEajISoqCisrq3htevPmDd26/cLRo/9gaipHkuRERMRgmFAmnYDWql2Qv/ddp22btpiYmBAZGUmvXt3Jk9edu3feoIvPbtA/U1Ml3t5OZM7iyLFjt9Go1fTo0YPZs//k/v0HHDumwczMmGxZneg3oDaZMtlQrPBI6tQrxKmzYwgNjWLk8M2EBEfSsFExGjYqRoECnvTpvZrVq9fz008/fa1bl0EsaV3a9BdC7BJCPBFCPIt7fNGefSYpmHjjaGdESLB/mus49e+/zB48jDY2mZhaoiK1JTN+69qd+/c/YrcqEXFf0o9dgU9L/uo1auBcuiL9D51k1ZWbjP33PP+oJQaM/C1JPiEEA7r3JuzvCwzPWokR2SoTceAS/bv1/KQg4/269OPhqvu0MmpBW2Urnq1/wq+dfk2xrtDQUMxF8l0SCywJ9E/wISNJUqrXPGDkYB5kCmXx093sfnmS6Y//wqNGPurUrZvmfleoWIGiLYqzNGAlBwOOsDFgC/fcHzFy0m8fLvwJbN20heVDl1A7qjJ55D7Y6yx5/vg5mnd2rdOGwetjWh//J4hYxx8ACkmSBmMYbH0Jfjw9lCX/P3C0VBAS+HF6OHPASJqZeDEhX3Wqxdgw8pee36ke1sS+cDW6b7rGkn8fMmL3bfb7W9B/2O9J8gkhGNyrB+LcUaaVz8efFfIhXfiHQT27f5IeDujeB98t1+jtWIdfneoTuPMOfTv3eq8eWmGeLN1SZk6Qf2CS603tmvsNH8oVSy1/Xv+HTfcvMfryAZzLF6H2R+phvoZlmXJ/Kzuen2Lx47+5YOXLiAlfxlHr1k1bWD1iIQ11ZcmvyIajsOD5k2dotRl6mF5IkpQr9jxrJaCDEMJCCFEgcWzSr8xhSZIyJqA/OBqNhn79+mFjY4OjoyOenp5s374dIQR169YkZy4jXr2ZQ3DYEpat6IqpqRLDJNIIw0RUhpGRnC7dKgPEa+OBAwcoXMSb3XsH4u5ug6WlAhMTGaBDJpOYNr0V127+wc7dA3j2YjY2NmZY28iBEORGgiJFvQmPiOblyyAyudqybu0p8ubz4M/prcme3YWiRb3ZtWcgZ04/4MYNQ6jf5i1KERWlRqFQvLNbm8HXIK07oKMkSVqKwaQtse3+tvcX+baookGl0mFqmjATPXwymno/V0u1nFar5a+NGzm8cwfXLl1laoUauFkZHAFld3CijZcP/X7pTL0mTShTqSLXLl/B9/lzCpYoTqXKlTEyMtzSU6dO8teqpQQFBFCsTHlad+yMra1tKi0n5+nTp6xevIAHt2/ilTU7bbv2iPcy9i6SJNF3yBD8OnTg7t271HNxwcfHh4iICBbMmsuxvw8TFhWBrZ0twY+f07Hszyhj+1o3a0Hm3PqHW7dukTdv3mR1CyHYvXM321ZvJTo6huo/VadVu1Y8fvyY0Nsh1HJsGp+3vEM5Nt/bwo0bN5J5EMubNy8vZE/Q6jUYyRTxdd+XbtO88rA03RM/Pz9WLFxGkF8QJi5mZKlRgl/r1cPZObnHyNSQJIk+g/rQqkMrbt++jbOzMz4+Ph81GD5+7Dir568jJDiU8tXL0qFruxRj/On1elbOWUln59Yo5Up8bHNyNfgq9c0KEhQYiLOLMxFq1Uf1P8ONQzJGYgiZ8AA4AxgDX8rT44+nhxqBSq3FVJkg+YfvhlLv16qpljPo4SYObt/JtUtXmFCiPq6WNgBktXOmWVQe+nbsQr2mTShbqQLXLl3h9fPnFCr5jh6ePMnmFcsNeliuPG06dfriethn0DD82nXi7t271EikhwvnzObfQwcJjYzExtaW8OePGdi4GsaxfW1SICf3jl1KVQ/37NrNznWbiY6OoWq9mrRo25rHjx+juh9AzSwJE79q7sVZ9mTfe/XwkfY5Gr0Whcwovu4b6gcMrtQ8TffEz8+PVYuWEOIfgIm9BS7VKtL5E/Ww98C+tGjf+rP0cM2CtYQGh1Kuejnad3m/Hq6et4KeHk0xlivJZe/DFf8bNDAvQFBAEE4uThl6+JnEmtyOB7YA7YAakiRtEkKs+4bd6gkMliQpBtCQjmFYJEmqCczCMMNZKoRI+TB5Bp9Njx49WLduQ7yToZcvX9O6dWuGDRtGcPAbxk8cGK8bjZsU59DBG6xYfjzewY9SKaN2nYJcvvSUChXKxcdu1mg0mJgo8PR04P6j6Wzdep7xY7bz9m0oMplEl66V4/ug0+lRqdQ0alIMf79wDv8zHIXCCI1GS/Omc/htxGYKFcpM9Rr5kvRdqTSiYqXcXL3ylHz5PPD3D0ehlDNkSC+WL8/Ppk1b43+zMvjypHUHtANQEMO5qnqxj7Qvr34D7B0y0W/0a/49G8CN26FMnvsKvVGx954ximNonz683LGNITmzk0lhhFl0DK9evgLgja8vttEaol/4EnnoBG2r18Z34w5yPn7L6TmL6damHdHR0Wz/azMbp4ygWzYZs2tmwe3lGXq2a0FERESa+//48WOGdG5LOd0rZlfNSR2TEMb07MS1a9dSLefk5ET58uXx8fFBrVbTrVVHAndeIezuS/L4m5P/kZKc0fYM/3sd/pFh8eWyGdvw6FHKG0ZTxk5h57jtVAutRBNtA+4suUn3Nt24f/8+btrkXivddK4p1mVubk6nAR3YFLqKe6G3eRrxiN2hW8hRLRt58uT54D0JCAiga9MOmJ0Io4dNXWpF5+fgkh3cuPbpUS8cHBwoX748OXLk+KjB1prla5nZczE5H5SnWmhrHi0Ppl3jTrFux5MSERGBqdYYpdxgylbQMT+vNVq2v7rEvbcvOPfqHn/e3f9xHRdS2h//Bwgh9sSFORBCdBZCjBNCRH2h5n48PXRyod/Gu/x725cbzwKZvPc+epf8H9bDX/vyeONu+nrkw0VmjGmUOokeWkdqiXr+htC9p2ldtS7PVu/D+04wx6evoGvr9kRHR7Ptr79YO240beytmFi0APa3rtKjdatvooc927dFf+Yfop89ogxqqsSEUtTMmD7rdvE2LKE/eWzM3quH0yb8wdEZa2llmp9ejmXw/esMvTp04f79+2SROyXLn0Xu9F49bNevE0tebuJW4D0ehjxlw+s9eFf0SbMe9mjZlkw3XzEyRxlaWHhxfPXmb6KHa5evZd6viyj8vAwNolvwam0AHZp0TEUPlRjH6mFhl3y80WrY9vIyd/2ec/71ff68c/DjOp6hh+8yHCgrhBiBwTFaQ+DXb9UZSZJkQE0hhEwIYSqEsBJCWKbT5FMOzANqAbmBFpIk5U69VAafQmhoKGvXrkWlisawfiABMqKiohkzZgw5cmZKphtFi3lTvHgRzM1NsbQyoVbtAkRHCxbOP8mcOQsAUKlUZMqUiePHbnLv3mvkchmrV/5LnbqF2Ll7AEZG8iT13rz5kly53Niy+TyTJjdHoTBMGhUKI/6c0Zrbt15x/fpzLl54kqQvQgguX35C5iyOqNVa+vVZQ4cOFbhweTS+bx6wYcOGL3n7MniHtE5ACwghigoh2gkhOsQ+On7Rnn0mpqamjBi9kZvParD/TCHK15jCuAmzU/1RvX37Nv63blLA2REjmQwzY2PkcgmtKoqwsDBiwiOI0mrxcXLm6ounTCtVlSJmNuR1dqV9vmLkjhZs3rCBDUvnMbZ2PjwdrFAayamZz4saHkp2btuS5v4vnTODvsW9KZo5E0ZyGXndHRlWIReLZyQP0v4+Duzfj3eEOSGRYVSxzUMDt6IUsPWitJ0PTZxLsO7Kifi8T9RheHl5JavD39+f07tP0dCpPjbGNpgamVLRsTzyJzLCwsLwlb9NVuat0dsU6wL4uUUTpm6ehLKBjshKgfSc35mxU0anabCzdtkqyhvloYhzLhRyIzytXOjsVY+FU+d+krncpxITE8Pa+RupZdEKO2NHFDIlBaxL4Pg6C3t2JXeUamFhQZRMhUavAUAuyemUpyMapQP7FS+JKufJ9A0fF/olw+Tsm/Jj6uHM1dw0K8n+0KyU7zCacVNmflAP31y/TT57F4xkckyNjZHLZGiiVISFhREdHoFKpyW7QyauvXzC+IK1KWhsTx5HD1rnLIlPuJzNGzawfuEChpUtiYetDUojOVV9slHR1oodW7emuf/poYcH9+8nv0IQHB5BQ29PWufLQQk3F6p7udEpbw6WnbgQn/deePR79fDC/uO0z1EJe1MrzBQm1PEuiuXbGMLCwnimC0hW5oU+8L162KT5z0xYO5WoSkpeF4mk4/QejPojbbFa1y1fSR27zJT0zIZSbkQWO0f6F6zIkumzvroerlu4gYa2zXEwcUQpV1LEtjiufl7sfY8eRkrRaPQGc1u5JKNboXboTez5W/6KiDJe/Jmhh5+LTAgRZ18vCSF0gOJbdSbWG/mXCv9SHHgohHgshFADG4EGX6it/2t8fX1RKBQkDzErodVqOXXyHipVUueOW/46R3i4ihYtWpA9W07UMU40btSNO3fukzVrVmbMmIGjoyN169YjKiqG/HmGUa7MWE7+e5fRYxtTpGgWdDo9p04lHPVwdbXh3n1fwsOjcXCwSNKeg4MlarUWjVrLxg2nWbXyBFqtjrCwKPr3XcOrl0FMHL8DT7feHDp4gzHjmqBUGtG9R0W2b9/8he5cBimR1gno2R9tRent68fMnPo7HTr1ZsRv06hQoUKq3vyCgoIY1L0n0W8D+efSHfpt2k5mRztmX79AtFZDaEgIYTHRLLp9hfr5ChOhisbTygZTuVH8Km8lr2wc2b0XN0slxoqk2/hFPe25dTnlIOsp8eTeXfK4OSRJ87K3JtD3VZrruHnxKrms3Lj15gnZzJx4GxGEVqshRqvBWW7Jk4C36PR6jjy9icrRnAIFCiSr4/79+3jJPZINiLLIvIgIjsA4mzFnAs6i0+vQCR3nAs+j94JChVL0Rg9Ajhw5GDp6CGOnjqZ06dJpXmm/ceEauWyTuuc3V5giReliPad9HV69eoWt3gm5lPSgsYc8G1dOJ9+RkclktOjSiq1vdxOpiQTgZcRr3lgGsGjDCn4d1B9Pz48IHynIWPH/tvx4evjiCTMnjaFD116MGDc5TXo4sFtvol6H8M/VWwzesRFPOwcW3DtNtFZNaHAIEepolj88T93cRYmMVuFpYYeJTEFUrB6Wd/fh8M59uJiaxJu3xlEokzO3Ll5IqekUSQ89vH3lMgWd7bn87CW57Kx5HRKKRqNGpdHibmbCw7eB6PR69t56SKi53Xv1MIe5czLNymXmQlRoOJKnBcdfXUar16HT6/n31RViMhl9UA+HjBrK6CljP0oPb12+Qn7npKFmLI1NMIrRfHU9dMAJuSypHmZRZuPK2ZT1sHmXVmx8uY8IjcFI4UX4a16ZB7Fow8oMPUwf1JIkxdm4m0iSNA/4Vmc/4zgoSVJjKf3dULthiDEax8vYtAzSCSEEZ86c4ezZs2g0GhLCqxgwfKISGo2OKpUmcOLEXa5ff063Lss4feo+N2/eZPnylVy+fJXjx08wZcoU1Go1O3fuZOTI34iMjCY8PAqtVqDT6Tl75gERETFMnbwHhcIILy8HGv80g4H91zFv7kFaNp+HmakST097li09lqQvy5cdo1btgly8MpF/jo9k2JCNWFt0wsO1N2/ehHL4n5H07lOTmrXyI5NJvH4dDEBMjDZ2cp3B1yKtxs5lgXaSJD3BcObpuw874OGo5Kf8b/hj3FAmTp3/wfyjBw+htUsWsmTKhqO5GWqdjgkXT1I8RxZGXTqJzNQMM62efhWqk9PJBbVehz42zIlcbhjIhUarsLO3w/fZW/R6gSyR449H/mG4Z8n3vuaTYe/szMvgcDzsEixUglIJvJ4Snlmz8PLcaSI1KlQaFV7m9gBYKJS8iQ7jSVgAv9/aT5lqlZjVp3eKAx83Nzf8RHJHJW+FPwWzFqXdL+1YMGM+y/5egQDK1S3H/IEjvkioA8+sXry8+obcDgledDU6LTEy7Vd1me3g4ECICEjmjdNf50veHCnvdLRs1xILKwvWL15HVGAkWXJ6M2vEHFzfCSqfFgSg1//fDKS+R344PXS3NaWOcwR/jB7OxOlzP5h/1MChNLbMhUc2YxxMzdHodfx59yDFsnsz8cY/yM1NMY3R07tMLXwcMsXroV7okcsS6aGDHa8e+yfTwydBwbjne/+k7F3SQw/dvb15euQ2EdExRMXEkNXOGgmwMlbwMjyKBwEh9Dp4kZKVqzC9d5/36uHL6JBk6a9iQiiRJTNtOrZn4cx5TDu4BQSUqVaeOf0nfhE99MiShacvAiiQKWGyptZpUaH/6noYqEuuh2/Vb8iWPeWJZMu2rbCwsmTt4rVE+Ufi5ePNjHlzM/Qw/egJWADBwHrgCQbv3d+S/oA5oJMkSUX6nQFN6cNPZgIgSVIXYj0AOzo6cuzYsc9sNv2IiIj4bvuj0Wh48OBBbGxPiQkTJiSzsIhzlBbnyOfqJRkgyJWzLmPH1onLFftXIEky9u3bR2hoKGPHjiHpRyiS1Pv3Xgm53Jy1a5fw6LEfMdEa2rYpGJvP4EV9zcoILK1MCQ9TIZeK0LNnXf49pgDysHz5Qu7f98XFxQaXTNZEhknotSqKFnGlUMFGvPX1wP+thDo6Py1auKfpc/ieP68fibROQGt+0V58ISoXdmDlocuoVCpMTU3j01UqFfOmz+LkwaOgF+QolI839x9SpnwNnjx6RKRajblSSSPvHOx89ohcJUswdf58ujRqgpu1DXKZjHxuHux7co9CLu6YmJig1evY+OAGbcf9zoVTx1l88jgdS2dHaSTniX8o62/4M2tQqzT3vU3XXswYM4SRVfJhZ25KmCqGacdu0qr30DTXUa9hA9qvWE+kJpp/Am7RzqwcegRyuZyrkc+xc7Fn69HUzx5mzpwZ+9wOnL19juL2xZCQeBj2iGfmL6harSpKpZIBIwYyYETKAeHTkzZdO9C/ZQ8czexwNLMlRqtmy4t/aNi+6RePVZcYKysrStcqzpmdByluXgUjmRFvVC+5b3GR0c17p1hGkiQaNGpAg0bpYxmU4XQjKZIkLSflgQgAQoj0dEj0Q+phxVyurFl3NWU9/HM2/x74B6EX5Cycl9d3HtGtcCODHmpiMFcYUzdTfva/vEXuUsWYtnAev/z0M5msbJHLZOTJ5MmBl7cp4OSJialBD7c8vULHSSM4/++/rLxwhtYF8qE0kvMsKJgtT14wd2LazWfTQw/rNviJzmtXE66OYc+jZ/xqmw+dEMjlRpx+E4CNoyObDhxOtY7MmTNjmS0T/zy/TgWPvEhI3A54zi0CGB6rh/2HD6L/8EFp7ten0qpzR4a074KLpTXOFtZEazSsvn2OBq2af3U9LFWjBMf3HqasTSWMZEa8jnrFDZPLDGneJ8UykiTRoGEDGjTM0MMvgRDifKLn475lX+IQIgUX+OnDS8Aj0Wt34HUK7S8GFgPkyJFDVKxY8Qt15+M5duwY31t/Xr9+zaBBg3n9Ou5WyohdM0ChMMLS0gKtVkPx4sWZOHEirVq14tGjx7GT0DhrCIEh/Mq7Uw09lpbmyGQyQkMjSP7TrY2tQ1CkqBcjRoxh/YaF7Np5CTc3OyZNbobSWMHK5cfYs/sycrmcqtXycvjQTW7dnYKHhza2Drh69RnNm4/FyEiOkZEcc3NjjIxk+OTIxIXzj2nUuCS7dl6mcZOmdO/eM02Lhd/j5/U99SetpDoBlSTJQggRkVqIgbg86d+19MFEKUOj0SQZcA3u1RdvXxiTqz4ySeLs47scePgUXVmBp1dm3rzxJTAsnLAYDW8VSjYvXoyVlRWD/pjI2JG/42VsRoRCxtHnT7lADF6qIB6EB/NTu7aULl2akiVLsmKxNV22/YWk0+Do7sWY2Us+yjNhiZIliR48mt/nTCcmPBQjU3Nadu1PtRppH/taWVkxY9VCGpavikypY/S9rXhbOOOrDSOrszOZjJM7zEiJaQv+ZMakGczfvxj0kKtILhaOWfjF4sO9D29vb0bNn8TMcVMJfhSI3MSIJl2a07Jd66/aD4DhY4ey0GER2zYtQB8j8MrlwbwJM7Gzs/sKrccd/s8gEXsSPZ8GDHzn9Wfzn9BDhTyZHg7q0Q/350qGeDVDJklcuH2Lvx8/RVdQj2fmzLz19SU4MpRwjZoAUyO2LF2ElZUVQ6ZMYPKI0XgYmRNhLHEi4AFXFBF43nvLo8hAGnVoHa+HyxctpN/WraDT4uThyfgFC7+JHk5ZvIyfq1ZEyAU9Dv5LDgc7XkSqyO7qiKuz44crASbPncGsyX/y+6FtIAQ5CuZlzryl30QPh8+cwpxJUwi95Y+kVNCoTUtatG3zVfsBMCxWD9duXoJeo8fDx4M54zP08FshSVIYcbMFMMUwmo/8gpPAtPRJAloBWYQQ4yRJ8gAyJZ4sfyIXgOySJGUBXgHNgZafWef/LcHBwTx58oRffvklUWgSGYaQ14aVHo1Gj7m5Gc+fP48vd/r0aXr27MnmzZuJ251MIPnr8PDw2OcSIH8nbwKhISoCAyNQqWIQQvD3wcGMG7OdnTsukSu3G0YKI0xNlWze0oeqlSeyb+81unarYqhJCGb8uQ+5XEa3HlVZMO8w5hbGvH4VzMmT9xnQfwh2dnb0+XUmuXP/UKdq/hNIqTkrkCTpCHAV2AlcEkJExqZ7Y4gt1RRYIoRIu3edr0TRXHZi66TSjNsqZ+manfHpDx8+ZHLngfTJlRCOSgiYcnQDZfNmplauBLf7i66cpXK/nlSpmhCqQKfTcf/+fUxMTMicOTP+/v74+fnh7e2NmZnZ17m4j2TWlD9Rnr5HcdesvA4PxsnCisdBftz2UDJu2pT4fPfu3WPL2r8IDQyhQu1K1KhZ46u7pD5//jx7N29Hr9dTs3H9jzoT9aMjSdIlIUTRD+XL4+QlNjZO+85P/oU90lTvfwVJki4LIQq/7/Vn1PvD6mERbyex4ddaTDkXztINCdFiHj58yIQOQ+mWpV5CZiGYfnI1pXJkpVrWgvHJK++eoOawzj+8Hs75cyr29y5SJqs7L4PDyGRlwX2/QM4onRgzJWGt4t69e2xbv4mw4GDKVa9C9Zo1v4ke/r1lG3q9nuoNG2ToYQpk6GHqxE78GgH5hRBfJrBr2vqxAMMsprIQIlfsGdWDQojUXXGnre7awEwMM5nlQogJqeXPkSOHuHfv3uc2m258LztYgYGBFCxYkL59+zJw4GBiD1jHvhu3Awqgw9jYOMWz5ocOHaJZs2bodDqEgKioSISQ0OvjJqFxu6JSbJ2JnwsM/yKGtuRy2LK9DzaWZfhjyhhevQyiZs0C3L79ivUbe2FhYUJYWBR1a02laHFvfulcmZrV/qBU6ezkL+DJ9m0X8PcLo9MvFfl9dGMiIqK5deslLi42NGowh5kzl37Sff9ePq84vrf+pFW7U/01FUJUif1idwXKxAqGFrgH7AXaCSHepEeH05ugMA1DlgczdtqKJOnPnz/H09g2SZokQVHPnCy/fRqdDBxNzTjt54vSJwuVKldOklcul5MrV674105OTjg5pW0n8VvRsXsXepzpyJsn18hp5cT156+4pg1m3tyEe7N3916WjVlIZfOSuCvdOTB+B/u27GHO8nlfzZxr7vRZXN32D9Wd8iNJEmuG/smZGv8ycGTaBxf/Fwj+n7w5fgr+sXHwdgHlMfyifTY/tB5GxPDb/qeMm70kSfrz589xN3pnl0qSKOKWm/WPj6OXgb2xBRdDnmOax+M/oYftu3Sjd8e2vLz2gPxONlx85c+Z4GjmrPgzPs++3XtYM2UODTLlxdrYglNz1rF/+25mLlnw1fRw3oyZ3N53gPqZDSFRNo+ZyNmKZRkwPG0xk/9vyNDDVBGGXYatkiQNBL7ZBBQoIYQoLEnSldh+BUuSlC5mA0KIfcC+9Kjr/5mpU6fi7x9AwoQQDBNESOqzVI5Go8XPzw9HR0dOnTrFxo0bkclktG7dmhcvXjBw4ACOHTuMEA4EBYXi7x/4TmtxE1o5hp9oXZJ3jY2N0Gq1KJVGvHkTwoljdzE2NmLunINcu/kHFhYmAFhZmTF/UUfKlhpDmTI52LC5FxPH7+SPibswNpaj0wm8MhusWywsTChRIhsALplsCA0NTa9bl8En8MHl3B/1iy0ZOzJ/5W7u37/PkSNHKFq0KNbW1vj4+LAiyi9Z/qf6UCbOnc1bX1/evPWjVYXOFClSJH61WafTceHCBSIjI+Pr+tbcu3ePZ8+ekT17drJkyfLefJaWlizfvJ5/jh7l3o1bFPCpzMAaNeIdVajVahZMnEsP11bxsdncLVzZeudvjv1zjMpVKr+37vTC19eXf7fuZ0iuBsgkGVqtlhZyC2Zt38+TVs1Svb7/RzIGXKnSD1gGrAAeEet4Ij34UfVQZmHPgvXbU9TDperkc+bnsmAmLZjJ29e+BL71p13FVv8pPVyyfjP/HD3Kg1s3yFrZh27v6OGSabP4PV9NTIwMepjZ1okVd05y7J9/qFylyhe/Fl9fX87s2sP40lXi9fAXY1Mm7PmbJy2aZ+jhO2ToYZoYKEmSLDYkyrdAExuzUwBIkuRIOi0OZpA+7Nmzh5gYdQrvJA+7Ym5uxoMHD5gwYQJLly5DpYpGkiSWLl2Kd1YvPDzMWLikJSYmCubOPszu3ZcJDUn5dIqZmTEKhYzoaDUxMVpAjyTpKVM2Jw3rT2f69D+5cn0i3t5OmCja4eFhn6S8p6c9arWWRQuPcO3qcxydXFAqjVGrY+jcpTJrVv9Lm7Zl4xcPnz8P4NzZ+6xfV+7zb1oGn8zXtSf6ipiZm9OtRUtymZhhITdiYdB4mvfoTuOmTfEuUYANV05RL3NhFDIjjjy/QYSzKZUqVUpxdfvRo0cM69GTXGamWBoZsSgggKZdu9KkefNvcGUGpyGDe/UCX1+yWVqwNTQU+1y5GTt16ntNxBQKBdVr1KB6jRrJ3nv8+DGuMqf4yWcc+c1ycPaf019lAnrlyhXym7kjk2T4+/kTFhSMmVxB9mgL2jRuyuY9Oz/JQ+J/EUGG042UkCSpEIYdTwH0FEJc/sZd+m4wMzenS7NWZJdbYyYzYl7YJFr17krjZj+TrVQ+tp4/Rk3XEhjJ5Zx4fZVoV0Wqejika2+yyqyxkCmZH/EHLXr+QpMWzb7BlRn0cEjvnkhvX5HdxpztQeHY5cjLmCmfrodeJjbxk884Sth7cf7Eqa8yAb1y5QpF7ZyQSTIC/PwICw7G3MiIvJKCtj//zKZduzL0MJYMPUzOO2dAFYAx3/gMKDAb2A44SZI0AWgCjPyG/cngHRwdHYE7Kbzz7hdMoFbHEBUVxdKlS4mKMnjIFQJUqhj8/V9z8fJsFLHhCFes6kz5MuO5dOkpanVMojoFlpZKxoxrQpGi3uz/+yozp+9HpdIiBHTsVJbr157gldkBvf41AQHhlCufk00bz9CmbcLkceOGM9g7WHDw8DBGDN+MpXlxBg0axN69e+nSpRNKJVQsP56u3Srj5xfG7JmHGTV6zFc6o57B+/jPTkBfv3jB0io1cbO2AaCxTsvvCxZRuFgxRk0ez+b1G5m7aStarY7K9Wowp1OHFAdbQgh+69uXgXny4G5jqKuJVstvixeTp0ABcuTIkaycEAK1Wo1Sqfwi53UWzppFwZho6pQuGZ+25uo1NqxZQ+v27VGr1SgUijSbitna2hKiC0uWHqQOxSFT5vTq9gf7EKxXERUVRWRwKK5m1kiShAYdteyzM2rAEJZsWPNV+vI+tFqDV7WvfQ4sRTJW/JMgSVJfoD2wIzZphSRJq4QQ079Vn74nXj1/waxijXG1Mhw/aKDTMmnuEgoXL8roKePYvH4jSzdsR6fTUqVJDQZ2av9ePRzRuz/dXEvhamX48a6n0zJ53nLyFMz/jfRwJoX1EdSrmHDkZPXFm2xYu4bW7T5ND4PUUcnS/aPDsXf2Trd+f6gPgbEDvMiQENwtLZEAtdDR0N2DMYMGsWjdt42qkaGH3y/vhjaRJKk68OVXklNBCLFOkqRLQBUMk+OfhBApzXYy+Ez8/Pw4ceIENjY2VKxYMc3f0X79+nH+/AUSJpyJz4BqSXD4JbC1tWXz5s1ER6t5d4e0Zs0C8ZNPMHi9rv9TIWQyO06fPhsbxkWPhYUJm7b0oXp1Q4jCMmV8sLExZ8yobajVWoYN2URERDSPHr6lebORaLU68ubzYGD/ddy760u5Cjk5fuwOixYeMYRjWf0vly6+oGOH5iiVSho2bEi1atXYtm0bR44c4a9NT3BydmH9+q2ULVv2k+9vBulDqr/IkiTtkyQp81fqS7qij45BGa2O/+4o5UbUcffiwO49hkDYrVuydvdWNv69gy49u2NiYpJiPQ8fPsRJiPjJJ0CMSkURuRGdGzWmSdVqrFyyJD4u0r7du2lWsyadatemafXqbNm0Kd2v7fThw9T0yZ4krXHuXPy1YgWt6tajY+06NKlajeWLFiXyYvZ+nJ2dccqZiauBt+LTQmPCOKO5SoMmP6V391OkePHiPDcK5/rz+1grTJAkiReRgdyJekX9nEWJ9g0gMPDdMwRfh+DgYHp16U/FovWpWLQBndv14u3bt9+kLwYkhEj74/+ETkBJIcRoIcRooASQnqFXfnA9VKNQaZLoYTXHbOzfvTdeD9fv/YtN+7fTpWe3VPXQXm0UP/kEiIlSkTfago71m9KoUg1WLF4ar4d7d+3m5+q1aVe9Pk2q1GTLxs3pfm1njh6mdq6sSdIa5/Phr5XLad2gLp3r16RpjSosX7QwzXpok9Wdc68fxKcFqyI4GPiA+o0bpnv/U6J48eI81MVw9ckjbI2NkYCnoSFcC/ancYECqP38vqke9u06gNrF6lOn2E/0yNDD7x4hxEGg6gczfgEkSSohSdI1SZIiMByL+EcIMTdj8vllmDhxIl5eXnTs2IlGjRrj7u7OjRs30lS2fv36DB8+DEmSsLQ0RaGQIZfLYz18x313DN5w37x5y6pVq5LEd47j6tXkjuJv3fSlXr365MiRIz5No9FRrVreJPl+bloCgcDc3Jh+A2qj1wu0Wj2RkTpiYgTXr73EwsKERQuPMH3aPqJVas5fHMfxf3+n769rePggkIYNE3TawsKCtm3bsmrVKnbv3s+ypSszJp/fCR9aFlkJHJQkaRUwRQih+fJdSh+Ucjm6iHD8EDjFuvs3kkloNR93CTqdDrmUME9XqVQE+PribGJKTU9vWhYtztKde1krN8Irqzc7Zs1iYsmSmBsbo9JomLViBWZmZtSuVy+VVj4OIfTJLPLVMWpePH7M6hbtsTU1Q63Tsnz336yWyWjfufMH65w0ZzKjBv7GyatrMJOZoDJVM2reuI8KlfA5yOVyZiyfz8/V6mIXIWGqUCKMYGCl+ijkcuSSDJ1O9+GK0hkhBL+06YX8cX6KmldGkiT8Lj2kY8ue7Dyw8dus/gvQ67+OIxRJkuyATUBm4CnQVAgR/E4eE+AEBjMvI2BLnLdFSZLGAQ0w/Gr5Ae2FEK8lSSpObEw2DL9so4UQ2z+jq4KkvtzjzxqlIyv5UfVQJkcbHoGfEDi5xOmhjMhP0sME9VGpovB/5YuDwpzKTjlpkrMs6zYeZI2RnMze3mydNp/h+SphrjQmWqtm0aI1mJqbUade3XS7NqHXI72jiBq1mldPHvFX12bYmpui1upYdHhXrB5++EjwhBnTGDt0BPuv7sHcyJhwhZ7h0yd9VT2ctmQRzWvWxkmjxVShQC+XMaJWTYMeyr6dHvZo0wvvZ4VpaVkNSZJ4evUB3Vr15K/9GXoYm+eb66EkSYmdDcmAfEDQp9b3mczDEBbrBFAfmAEkt33P4LM5duwYEyZMJDpaQ3S0wUIhPDySmjVrcuvWLYYMGcL69evR6XTUrl2bWbNm4ebmFl9eCEHFihV59OgR0dHR6PUCnU4fqzXvOiaSodHoMPwcJA2F9PDhW6ZO3sOvfWtgZCRnw/rTHDxwkxvXAzC3UGNsrCAmRoNhIhtCpky2ScoqjAxOji6cf0RS77sSGo2eZ88C6NGrGrPntEty/c7ONkyePOu9C6gZfF+kqtpCiM1AIcAKuChJ0kBJkvrHPb5KDz+RGL0OAYSHhCKEQC/07H/5gqp1an9UPT4+PrxQxxAQYTg8Hejvj52xCQeePaO0dzaUciM65S/MzvUbWLdwId0LFcI81pmFqUJBt8KF2bhsWbpeW+EyZTjx5GmStM0XL1LZ2wdbU0PoA6XciI4FCrN7w6b43YjUsLS0ZPqimaw6tI4/t89h65EdFC32dT3Vu7m5MWXxHDJlzkS/KnUZX6sFblZ2vAwNRGdt9k28a167do3w58a4WeSNNx90MsuG7o0Tp0+f/ur9iUN8xOMzGQocEUJkB47Evn6XGAyu9QsABYGakiTF2YdPFULkF0IUxBCr8/fY9JtA0dj0msAiSZI+Z/S6BDgrSdJYSZLGAucwOCJKN35kPVQLw2QlPDRBD4/6P6JanVofVY+Pjw+vRRSBUYYYboH+AdgozPjH/z7FPXKgkBvRMntpdqzZxOoFi2mfoxTmSoMemhgpaZ+zNBsWL0/XaytUuizHHz1Nkrbp3GWq5fLG1twQ71RpJKdryXzs3bwxzXo4dd5sFu/5iwnrF7Np/26KFvvsaBEfhZubG5MWzMPZ3ZUhtWowrVFD3G1seB4cjMbC4pvpofTSFB+rBD3MbJEdaz+XDD1M4HvQw/BEj2jADBjxGfV9DjIhxCEhRIwQ4i8gbQF3M/hoFi5cSFSUindNYsPCwihRogQrV64mIkKFSqVmx45dFCtWjMjISAAiIiIoXbo01avXwN/fH41Gj2GNS07Ceu678TwlTE1NMDFRYGFhiqWlGaamxkydMp3Dh3xxsu+OnXVnevdaRUBAMLdv3+Xt2xAUCln8kYhOHRYTGmo48vDyZSA9u69AILC1s+DmzZex4VtI0qYkSRgbJ/16aLU6goMj2b1n1zfVogzSTloETgNEYljNs+QH8VrmZGnJuItnyGltS1admpOBflRo0oScOXN+VD0ymYwRU6Ywpl8/yto7oPL357p/AEUyZ8XH0TAAMDYyQqbVEBwYiHOikAQANqamREekb1z6XgMH0btDB25fukw2c3Nuh4dz+PkLljdomiSfUm6EkTCsXqV1ZdrKygorK6sPZ/xClClThn8rl2TJsdMUs3IjUKvianQAU5fO/yb9efv2LUq1HbyzoKZQ2/H69etv0ifga555agBUjH2+CjgGDEnSFcOIPu6fXBH7ELHvJT5cbJ4oPfEhOxM+c2wohJgjSdIJIM4zQSshxLXPqfM9/JB66GhhydTbR8lm7oi3FMX5iNdUav7TJ+nhb39OYnTvgRQzcyXibRB3Qnwp6JmdrHaZAFDKFUhqHSGBgThlSqol1iZmX0QPf+3YnluB18lmacqt0EgOPfZlfduku6xKIzkKdD+cHp4qV5bZJ09R2tERf7WaC2GhTF648Jv05+3bt1hp7JOlW6ntM/Qwrivfhx4mOfsuSdJkDBPmSp9T7ydiI0lSo/e9FkJsS6FMBp9ASEhICqmGGJwvXrxArdYQN4HU6QRhYRFs3LiRTp06MWzYMK5cuUZMTHR8ucR1JMTpTPyvKciaNSsHDhxg7dq1WFlZ0axZM2xsbHj06BGnT19ApVLF59Xr5bx8EYpSKcPYxAhVlJp/T9zHzaUnLi42+PqGIJdLREWpUSqUBPi/RalMbNhkqMfISMaObRf5tU9NPDzs0ev1jBu7HSsrEzw8w2jVqjGNm7Rg2tQMFxDfM6n+CkuSVBOYjiGeXuF3RPK7RimTMbZeXX49coTCDesxqXx53N3dP7oevV5PZGQkdVu3ISIinLunz1DW3omf8hSIzxMQGYGprS3umb244etL/kTeCR8FBJDJyytdrikOKysrVvz1F2fPnuXZkyf8nDMnzkeOcP36XSp6Z4vPFxgZibG1dZLBllqt5vjx4wT4BVCsRDGyZcuWUhPfDEmSGDb6Nx49esSFc+fJam9H7xIlOHPmDOfPnad0mdJ4enp+dL16vZ4LFy7w6MFDfHLmSBJSIjXy5MlDhPFiojVFeRV+E4EeN8u8RJk+oUCBVp9yiZ+NwevjRw24HCRJupjo9WIhxOL35k6KsxDCF0AI4StJUorbLrHu9S8B2YB5Qohzid6bALQFQkk0AJIkqQSwHPAC2gghtB9xTe+27wWEALsTpwkhkh9G+fQ2flg9VMjk/F7zJwae2Ufp1tWY8pl6WL9dS8Ijwrlz+gylgvNRO1uCtURgVDhm9jZ4eHty2/8leZw84t97EuxHpszpr4fLN2+J18PGOXPifPQw1x5fpUrOhHAlARFRKCxtUtTDQP8Aihb/PvVwyKhRsXp4jgL29nSJ1cML589TqvTn6eHjhw/JnuPj9HCuYgmRmmLcDbuBXujJaZ2P1yaPKFDg23iFz9DD1JEMH2xewONDeb8Qx4F673ktgIwJaDrRtGlT/v331Du7oAKNRo1cbsS7O6ORkZFcvnyZTp06sXbt2tgQLB9a+0hYczU1NUGhUODm5o4QAkmCv/76iy1btrBgwQJUqhgME1cdhl1UQ/tqtR4zIzkKhRyVymCK++JFIFmzOrFhc2/y5vXgwIHrtG01P3Z3NMGGQaGQ6NuvFvYOFhTKP4x8+T25d/c17h52HD0+Ajc3O3r1rk7hAr/RvFlLihb9upZ8GaSdDy0DjwB+FkLc+kC+7w5VdDT9t22j+7hxNG/Z8pPqCAkJoVf7zriEK/BUWHNfHYDcUcmxsEBsHj+ksKs7z4IDWf3gDv0mT8LZxYWBHTvSQq0mr4sL9/z8WPPwIeO+wGq1TCajdOnSlC5dGoDMmTPTvUUrhBAUcfPgeXAQqx/c5tdJE+LLPH36lH4depFNkwlbLNgz9y9yVsrPyAmjvoh3ys8ha9asZM2albt379KufnPyy92xlEzYOWcNpRpW5ddB/dJcV0REBL07dME2SEcWpS1r1TtZ5GTCrGULMTMzS7Wsu7s7Hrlt+Hv7RHJTDhkyDjGNPBWykOud3e6vhpAQ+o/6vAKEEO9VYUmSDgMuKbyVZpMtIYQOKChJkg2wXZKkvEKIm7HvjQBGSJI0DOhFbDD02EFZHkmScgGrJEn6WwgRnXILH2Q3CWEHzDGc0boH5P7E+lLih9bDIXs30XPi6M/Sw55tu+AQYoKrZMdj/VvkmeSc1fhi8/Iu+Ry9eBEWwF+vLzNo+jicXVzo364zP6ljyO3ozoNAX/56fZOJS+el89WlrIc9WrdALwQlMrvyNDCEJVcf0WvsH/Flnj59yqAu3ShkaoWj0oTJy1eRuVRxho8d813rYadGTShhY4+NXMFvi5dRtE5Neg8YkOa6IiIi6NPpF5yiosluZsnmyM0ssbZkxpLFadJDh1zWzN01huLKMkiSjIWvJpG9jHeGHibu0jfWw3fCsGiBZ0DfT6nrcxFCpKszuAzeT6tWrVi8eDE3b94iMjIKuVyOsbGSrl27smTJUiCpx1pzczPy5jU4AdJoDBPBBPQknNITJJ54KhQKFAoFKlU0ly9fjs0nQwg9R44cIU+ePIl2PuNIuqMaFaXFzs6UoKDI2DqN2L1vEFmzGs7Z165dkFlz2rJo4VEcHS0xMgKl0ojCRbIw8Y9mSJJEp18q8fvIv7CyMmHHrgHxum1ra07L1iXZuWtnxgT0O+ZDZ0DL/YiDLYAInY42JYtzfN+nxYwPDAzkj1HjqKB3p2328lTMXIAuPlXIHmxC2Zo1CSqUh5nPH3DO1oIxixdSvEQJvLy8mLVmDY+yZGHao0fczJSJaatWJfH6lRghBH6x3gxfv36dwhc27djb27Nw43pCC+dj5vMHnLExY9SiBZQsVSo+z6j+I/nZtBL13SpSzq0oXT0a8fKfBxw/dvyT2/2SCCEY1XcoXV2q0SBzaSp7FaafT0Ou7DjBlStX0lzPgplzKKqypWOOClTKkp9fclQkd6gpy+Yv+mDZyMhIfO+9oa37IHKYFiCbaV5auQ0g0jfmm3mhBBBIaX58sC4hqgoh8qbw2Am8lSQpE0DsX78P1BWCwSytZgpvrwcap1DmDgaz1rzJSqSR2HNV+WL/ZgVKAv98an3vaeOH1cNIvZZWRUpxbN/fn1Q+MDCQSb+Pp6QqK808qlLOvTDtPGvh9caS8nWqE10uC8vCrnDTS2LCirnxejhn/Up887uwIOAaj7JbM2Pd8q+mhwvWbcQ/RzEmXn/FPzJ7fpu7OIkejhs8lF7Z8tE6XxFq5MjDyBIVCblwlePHjn1yu18SIQRjBw5iaP6itMhbkFq58jC2TEVu7z/8UXq4cNZsysqN6VmoONVz5OLXwiUorIPlCz68UBoZGcmbh7708ulHHuu85LLMTc9svxLtH52hhynXFcK30UMrIYRl7F87IUQhIcSeT60vgx8DY2NjTpw4waJFC2nSpBGdO3fi5MmTTJs2DU9Pj9jQKIadRJkMzMxMadXKYMlVr169d0JV6TGsXehiHxDnbChv3rzodDqE0GPY2YxzFGTY5TSY48sTvQcpxRJVKMxRKBSADKXSKH7yGUfJUtm5ceM5ERExjBnXGCMjI65eecbJk/cAw0RTaWyEXC5PtmgYFanGONYfSwbfJ99BAK8vg4uVJbVy5+ToPycIDw/H0jJt8ZefPXvGqH5D0QZE8PjBQx5a2pHF2hkncxsAqngUYMbho2z8e0eK5V1dXRk08sOxle/evcu4QSOIeunPm7dv0CnAyTUTFevWoN/QIcjl79q9fxg7Ozt6DUjZF0pQUBAq3zA8PBMWdiVJopxNQf7espuKlSp+dHtfmmfPnmGpMsIpkYc0mSSjom0eDuzcS6FChdJUz9kjJ/g9R50kaRU98jLxwH56D0x9J/XcuXM4RmQlIkiFTm3wBBceGIGrPjfHjh2nceNGqZb/UnzFwOu7gHbAH7F/d76bQZIkR0AjhAiRJMkUg7v/ybHvZRdCxMWzqA/cjU3PArwQQmhjzWdzYPAqmS4IIS5IkvRtDsp9hzhbWlHNJy/Hzh36aD38re9w1G+jeHT/IZks7PG0cMHRzPCdrJCpMIsO/82m/Slb0bm6ujL4tw9vHN29e5exA0cQ9SIA37dv0RkJnN1cqFivJv2HDf5kPezZL+WdwaCgILSBwWTxSdAQSZKolTk7B3fspGKlb3FULnWePXuGnU7gYmUdnyaTZNT2zMKh3XvSrIfnjh1jWonySdKqZ83B0EOH3vv7EV/23Dk8Y7IQGRaFTm3YEQkPjiCbeQ6OHztOoww9/C70MNbs9hcM3mYFcBBYJgwzhgz+wyiVSurUqcPRo0dZs2YNK1asoG7dumzatInx48ezbds29Ho9lStXYuHChfFn3EeMGMH+/fsJDQ2NrSluAz3u/GfcBE/H1atXEzlze3dRx2Bya2lpTGRkDHp9nIfcODNciJuM1qlTl+XLVwISao2We/dekyNHwhG2f0/cpVy5nGTKZMNvv+3Fw8OdIkVLUK/2n3hndUIuN+LVy1BiYqK5evUZBQsajnc8fPiGDevPcubMnHS6qxl8Cf6zE9A4dEKkOQC5Wq2mf8cetLErhZePMw/FfWK0Gqaf3MrE6h2QSTL0Qv9Jg6HEREREMKzrr3RyKoZk74GLhyW3Ql6zx/8G0advsnD2XHr26/NZbbyLXC5Hn4K/FJ3QYaRQpGtb6YVcLkeXwu+lVug/zt2/hOFzS7Thrxd6ZLIPf46SZFjN89SXwExmWE3TqjW8eeNL+kf5SDtfccD1B7BZkqROwHPgZwBJklyBpUKI2kAmDCZjccudmxOttv8hSVIODMupz4BusellgaGSJGli3+shhAj4nI5KktQQKI/hgzkJ/CRJkiTS4vb0/wTD/33a9bBv+140Mq6Mh1smHoY/QK2LZu6FjYwq3xWZJEOXTno4pHMf2tqWRLJS4exkxZ2wV+wLvkrkidssNJtLz/7pr4cpaYtOr/+u9VCbQhxTrV6P/KP0UDJ8bon0UCf0yNPwfyFJEq9evSafcVGUsXqo0arxffMGkaGH35Mejsdw/GA+MAe4Etv3wZ9RZwY/AHq9ngoVKnD37j3UasMx4h07dnHmzBkePHjAhg0bEInGxRcuXGDChAns3bsXSZIl+i7FTUDjnkNcDNCk37d3PeMKnJysmD23HTt3XGLH9ouoVHG6lRA6yszMnMjIKGQyGXo9aNSC+nWmsXJNd/Ln92D/39cZPnQTm7b8Cjpz6tQthJ+fGatWrmbxoiWcOnUKSZIoU6YMu3bvomqlTlSqnAeFQs7BA9eYMnXad3emP4OkfJ3gWd+Iq69e4ZjFG3Nz8zTlP3XqFD444GVtMAOwsLTEUWlJFmNHbvo9BWDf88vUaZo8GHloaCgzJk+hea26dGranL/37n2vu/8Df++npJk7CpUWOxMzJEkir60bFsIYVWQEi6ZNp2Wd+iyeN/+zzNASY21tjb23Cw9CEnyy6IWef4IvUr/F1wmu/rG4u7ujtpbzIiwhyLlWr+No8A1qN6qf5noq1anB4edJAzHvf3aVqg0+HJJHoVDwXH8XraSOT9NJWp6IG98m5h0JTje+RuB1IUSgEKKKECJ77N+g2PTXsYMthBDXY0288seaqo1NVL5xbFp+IUQ9IcSr2PQ1Qog8QoiCQojCQogdn9PPWC+PXYBrwHWgK/BrxuQzgetvXuKUNctH6WEWtQselgbvthZWFtgb2eAhd+F24GMADvuep26zBsnKhoaGMn3SVJpWr0+HJi3Zl6oeHqC4iRcKlR47pTmSJJHb2h1LvTGqyEgWfiE9tPJw49bbBM+teqFnx5N71Pm5Sbq0kd64u7sTZWbC06AEU1etXsfu54+p9VPyz+B9VKxZk78f3EuStvv+HSqnITarQqHgofo+GhLix+qEjjvqmxl6+B3pIQYnPz8LIQ4BKiHERKDCZ9b5WUiSZCZJ0m+SJC2JfZ1dkqT0CwicAQBHjx7l8eMnsZNPw+6jweNtOJs3b0aSpNhJn57WrVtTtmxZdu7ciVarRaNJfEZUj4uLC2ZmJshkcUeJ4yaSCea2hkllnLYLjI1lLFzciSY/l2DNuh706VsTExOjRGXiHlCsWFEUCkOIF51O8PhxADWrT8bOugszpu9j9boelCtn8NReoKAXWTJnBgymxpUrV6ZSpUoolUqaNG7C48dPafhTd2rW6Mjduw/o/MuH4z1n8G35z+6A+kVGseFNANMWfficXxxBQUHYSqbxr51cnHnx7DlyIXHo2TUOB90lUyEfmrVqkaScSqWie6u2VDFzYmzecoRGq1g3YyEvnz2nc4/uydoJ9A/AXmGOVq1FoVTGpz8PD8DaBMYVqohPDh8OHznLr6dOs2jt6jTvWqTG2OkT+LV9Dy69vosNFtxVP6NSs5qUKFHis+v+EkiSxKR5f9KvU0+8gmywlEy4Ef2Chp1bkDt32n3LdO7VnUF3+jDrzkG8je14GB2IdU5PBv3S8YNlIyMjyeVYmIMBy3HT+SBDxkvZXXzsChAd/an+cj4XCb34T68dfQq1gQKJTMxWSpJ0Axj0Dfv03eAfFcnW0Jf8uTjtVslBQUFYC4v4104uzryMeY5ML+PY60uciLiBe9FsKeph1xbtKIsngz1rEhYTxZY/lvPqyXM690pBD/38sTcyRxutxcgo4czOi0h/bCL0jC9UEZ+c6a+Ho6ZMpn/nrnj4vsBRacylYH/KNqj3Xevh+FkzGdSlG9mVJtgYKbgQ5E+99m0/Sg9/6dmDIXd6M+n8KXwsrbgTFopF1ixM7NTpg2UjIyPJ71yIjQGr8JZlRybJeKi7R16HDD38zpASe9GVJEmJIXTUt2QFBs/AcQexXwJ/YYiHmkE6cfv2bTSauMlnAhERkVy9ejX+9fbt29m2bTtqddykMy7MSoKVRWBgIH5+flSpUoXLlxOfM4+zepHF5k/Y2Zw5uwP1GxSJf92hUwVmz9of/9rcXIHcSCKrtyPjx4+hXLkyHD58FFNTY4yMZMTEaLC0MuX6tef077OGX/vWxCd7bXbvuk7nXwwRj6Kiolizdg0njh/B0cmFXzp1IW/evLRp0+Yz7lwGX5tvNgGNNU+5CLwSQtSVJMkO2ITBe+VToKkQIjg27zCgE4b/8l+FEAc+VL+tqysrt279KG+G2bNnZ9aLiQRHhJHXOQu5HLzwyOzJ89AzNBvQmZIlS5IlS5Zk5f7es5eCcgsqZTE417A3s6BjziJ0nTqTfw8epUzVSrRo2zre1r5k2dIs+OsAzRzyExGuwsrYBN+oUGL00bTNUYgQvQYThZLKntnZt28TxXLlIU+BfAwfO/aj4/apVCo2rVvPqUNHsbSypNfwvpibmxMQEECfggW/ajDze/fusXrhCl48eU7BEoVo80t7HB1Tj0nt6enJ5v07uHTpEqGhobT19mbfjp10+rkZzm6utOr8C3ny5Em1DmNjY2YvXcj9+/d5+vQpjby8uH71Gt1bd0GpUNCgVSNq1KqZ4v9KoUKF0NosoLX9IF5EPUAvdJQ3r8tx7TqKfeXA9PEIvmbcux8FPWAP+APEhkf4Yc47fWk9tHPLxKptWz5aD6e/nUxwZCi5HLLiY+uNe2ZP3miP0GpIx/fq4b49e8mjsaNsFsP30s7UkpYepek3ZQ7HDx6jXPWKtGjbKkEPy5Vm7qZDNLEpTGSYCkulKW9UwahFFO18qhKKOn31cP16zhw5hIWVFd0GDYjXwzbfQA/XLFrGq6fPyV+8CK07pU0P1+/dHa+HDb292b9rB52b/4yzqxstOnVOkx7OXLI4Xg9rxuphzza/oFQqqdeicap6GGU1n56ZBvI43KCH1S3rsCNyY4Yefl/4JTpvagWcAtLf/fTHkVUI0UySpBYAQgiV9L25m/4PkCNHDpRKo9iQKok93pqTN29etm/fzuLFi7lw4QIqVRQJO5MQt2Mah1xuxIABA7hz5w7JTW1JVNaw1qFQyGncJOkC3tu3ocjlhsmtqamcBj8VYenyziiVRgQGhlOn5nQKFsyPo5OgdJnsLJh3mJmz21KzVn6uX39B967L6d2rABHhMpo0aUJ4eDiVKpXD0cmIps2K8OTJK6pUqcDceQv5ucnP6XgnM/jSfMtlwz7AnUSvhwJHhBDZMQRMHgogSVJuoDmQB4Mnufmxg7VUMTMz+6jB1vXr1/mt+yBKmvlgFi5n77XTjD+xhvl3/6ZKi/q0aNEixcEWwN2r18hrl+C9KzQkBP8XvuQzd6C+0puY/dfo2rIdUVGGsIH58uXDtmA2dvjf5E6EHxf8nzL11gEKOWYiUK3C2TUTQeFhDNq+Dh+FNZ2ccuL+OJAmlapx6tSpNF+TRqOhZ7uOBO85TnfnnNST7Fg7ahK3r12nevXqX3Wwde7sOYa17Ue2u5a0VlRCOhRI55/b4+eXqhNBwHD2qXjx4hQpUoRhPXphef4mfT1zUy5CMLFHH/49njYvvj4+PlSrVo0Ff87j3KzD1IsuSZWwguwcu54p4/5IsYyTkxN121Xj35jNmMrNsVTYcDZmB2UaFMTb2/uj7kF6IvRpf/yfMA44L0nSakmSVgEXgAkfKPM98d3p4YiuQyiqzIVZmJxDt4/z54UlrHy2nRqt6qaqh3eu3CCHlVv869DgUPxfvCGXkQvVtLkJ2XaHzs3bJ9FD+yLe7A6+xr3IN1wKeMzM+3sp5OhKkCYqkR6uJYexFZ0z+eDxxJ+fK1f9aD3s1aE90cf2M8jHgxbWxmyaOIbb1699Az08y28de1PolZzu9sWwOPOcbs3afrQejujdA4c7FxiR35Masgim9O350Xq4cPocri7aS3OjAtTX+rBv8kqmjn+/HtZsXZ09YVswMzLHSmnDgfBdFK1XKEMPvy9+Al7FPu8KNBFCLPl23QFAHeuUSQBIkpQViPm2XfrvUa1aNdzc3GI93uoBPTKZIeTK2bNnadOmLfv3HyQwMDi2xLvTgITfCY1Gzfr1G2LjecadCY17xCEwNzchb14PlEoj+vddg1Zr2BEND1fRv+9aIiIMJvt6vZ7Zc9uiVBr2vuztLRk/qTFPnjzk0sWnrFrxLxP/aEbTZiWxsjKjbNkc/LW1D9HRGg4cOIKxsTHzF8wncxYzdu/tS9t25Rk1uhE79/Th1949Y3dzM/hR+CYTUEmS3IE6wNJEyQ2AVbHPV2EQ0Lj0jUKIGCHEE+AhUDw9+yOEYMLg3+nuXpOfC1ajXO5iNMtWCVOZCYWaV6X3gL6plvfMno3HYUGGuhAEvPXDycSSV1FhaPQ6ynvkppDelp1btwOxplR/TqbeyO48zm/FMYcIHApl56WkxdM7C6ampqw5e4Jabj40zpKPos4etPEpQv9cpRjWs3ear+vIocNkjhLU9ymAtYkZnjb29C9cke0r18QP/r4Ws8b/SQe3OmS39cJMYUoxl7yUl/IyZfxknj9/nqY6Nq5eQy0bVypmzYGlsQm5nDIxuHAZ5k+eFn++LDw8nFu3bhEUFJRiHTdu3CD8pj/13Ctha2KNo6kdzTxqcWrnMY4fP05kZGSyMr/278nvK37FqNorRMWnDFjYkZ79u3Pz5s1EHuO+Ll/rzNOPghBiC1AC2IzBrKu4EGLzt+1V2vge9XDcwFF0cGhAw7y1KJ2zBI08q2MqN6F42wr0HpS6Q6DMPll5HhHrP0VAwFs/7JXW+MaEoNXrKO1SgDwqlyR6OOHPyfw0qisvippzyjUEx6LZeSXT4Jk1QQ/reGSniXceijm70zZXIQbkLvFxenj4MD76GJrky4WNmSmZ7W0ZWb4ku9as+up6OHfiNHr5VCangzvmShNKu+ekpkVWpk74I+16uGY1P7nZUi2nN1YmxuRxdWJUhUIsnDb5o/RQdfcNTbzLYmdqhZO5Le2zVeHsniPv1cNe/XsycEkfQsu9IbDkC7rP/YUe/TL08DtDCfwiSVI7DGFgXkiSlLbD31+OUcB+wEOSpHUYFtYynCKlMzKZjJMnT1KoUAHiJqB6vZboaBWrV68mMlJFXNxOA8nDoxiQ0Ov1REfHxOZNCKkiSQkTUaWxjMyZHbh4ZTxXb0zi0KFbuLv2pnzZMbi59OTmjZcIIVAaG2FmZoy1ddJYw+7udtjZmdOlW2UCAsMpVz6pVUvOnK5IkkRMjGGt4sD+3XToVDbJgmqxYllxdLTk2rVrn3n3MviafCsT3JkYhCdxLABnIYQvgBDCN9aEDsANOJso38vYtHTjzZs3mEZJOGQyuLe3sLDAwsKCBpYVuPrw2QdKQ/1GDem4dj1Z/OzIZuuIXq9n4c3T+EdEcuL2VVZGHqaMd26unjlPizaGmEsymYwqVatSpWrV+Hr6de3Owaf3qJk1N7f9fGlaMA/hWjUOSsNtKunsxaw7Z1PsQ0pcO3+BAvaZkqQZyeR4W9jy9OnTjzo39Dno9XoiA8Kw9U4IH3D17V123PoHkztmDDs/CCM3Y6YumJbqLsS1c+fp7prUq5m1qRkyVQwqlYpVS5ZyZOsuvC3seBEZQq7SxRg+bkwS5xjXLl/FR+4R/1qj17L6+g583wYxr9tCws1CadSxIZ17dk7STvHixSlevDh6vZ7JY6Ywqc9UHKVM+InXVGpYnoEjBqTLubS0YJD9/5uBVJqQJKktcEwIsUeSpHJAK0mS1gsh3nzrvqWBmXxnemgcKcfO2gZI0MM6NpW5d//Dk6P6jX6i/aoNeAQ64G3pjE4nWPXiKIFREZy8d5l1Mfso6ZGfy6cuvqOHVahStUp8PYn18I7/a5oXrkyELpEeZvJk5u3zab6uGxfOUzRTUhNXI7mM7DZWX10Po4JCsfdI+LgvvnrAX9dOYH7blLHXHyMczZk0d0aqenjjwjkG5kyq7zZmpsjV0ahUKlYvXcrR7dvJbm3D0/AwcpQowfCxY5PpYR6ThLAHGp2WpZf28dY3gGV95zJNGU6Ddo35pcf79XDK2ClM6T8FF7kzb3RvKF+/PANGDMzQw2/LbgxWIE5AMQwWFDswhIT56kiSJANsgUYYYjRLQJ/P9XyeQcpcvHiRGzdukuAoSBAWFkZSc1tIOMMpkbDDadi9VCiM0Gg0JOx4JkxaTU1NsLS0JDQ0FDNzBbnyuDFo4HoePwogq7cP5ubWHDp0KLYNw3ezaFFvgoMiOHDgOjVrFojvwZpV/1Klal7GjW/C8qX/cO7sQ7JkceTRIz/MzJTExBjMex0cHACwsrImMCA8yfXqdHqCgyPij3Vk8GPw1XdAY72e+QkhLqW1SAppKbpTlCSpiyRJFyVJuujv75/mPpmYmKDSa5KlqzTRmFtapFAiKVZWVsxYsZR/TGIYeuEgvf/dRUSMjFlF29AhSzlG5qrL6Ye3MDI3SbWeSbNmoCmeh8EXDvE0IoQAtQpXaxuk2FsQo9OCPO0fmauXJ6/Ck69Iv1FFfFVzM5lMBkoZap3hHgeoQth24whtbZrS1LkhbZ2aUiKwAAO7pRyzLw5XTw9ehgYnSdPodKiEjqOHj3B/11EmFKlF55ylGFukFoobz1gyb37SOjzc8NOHxL/ece8wJsGONDVvS12bxrS36s7RxSc4fOhwin1Yu2It97e9oKVZd6qbNaSVWQ9ub37MxnWbPuHOfDoZK/7JGAS8kiTJBViOweHG1/1QPoHvVQ+jdclNmVTaaMyt0qaHs1Yv5rxDCOPu72DYjfVEqGFCjm60cKlKf49mnH16HYWFMtV6Euvhk4hQAtVRuFpbE7fwHa3VQhpCKcXh4unJ89DwZOmvIqK+uh4KhTxeD/0jQ9l89TgDstega87K9M1VnVp4MbRn31TryeThyfOgpPqu0emI0uk5evgwj/cfYHqFivQsXJipFSpi8eAhS+cn10NfbUIdm28dxyrSls4uTWnuVoe+7u04tfIIR96nhyvX8mz3E7rYd6KBbT262P/Cwx0P2bQ+Qw+/MUZCiD5AK6CcECICsPlWnYl1Dtcr1ovwXiHEnozJ55dj/PjxqFTRJPxcJHYylJi48aQOwzlOHUqlEkmS0Gji4nbKE+UxlC9TpgyvX7+mbt26RISr2bL5AvPmHObggev07NmHCxcuktRTrsDS0oQ/Z7ahQ9tFTJywk927L9OtyzIWLzrKsBENkCSJQoW86d51BS6O3SlSaATZvftTIN9QHBwcUMY67GzX7hf+mLgPf/8wwGCxM2vmftzcPMiRI0e638sMvhzfwgS3DFBfkqSnwEagsiRJa4G3kiRlAoj9G3cY5iXgkai8O/CaFBBCLBZCFBVCFI1z5hAUFMTY4b/zU4WaNK5clyXzFqLVapOUs7W1xSGbKzf8H8enaXRaDgRd5acWaXPJ7+HhwbQF81i1dyduDm60ylw6Ps6aDBmVnXIhtKkfQDExMaHfkMFs++cwbX7twc6XcQe/QS8Ea+5fpmLdWgCcPPEv7Ru15KfytejSvANXLl/h3r179P2lI02rV6Z9458wszTnSPALngUHxNah5++HN/EokCd+Nelr0bh9M7a+OIxGp+XMy6sUNS5EeEwUqshIHt1/iHWMBdpXMTx79v4d5xadOrLu0S0CoyIAUOu0rL55kdpNm7B9zXqaZy+CTEr4l66ftQCHd+5NUkf58uV5aurH/eAnAFzxvUteRUFQSJiamCCTZFS0qMqGJRtT7MO2tTspZ1kt3vxDkiTKWlRj66rtn3N7Po6PGGz9Hw24NEIIHQZT1jVCiEkk3VH8Xvnqejhm2CgalK1Nw0r1WDx3UYp66OSTidvBD+PTNHot/4RfoGHLxmm6KA8PD/5cNIe1+7fh6uBGE5fKyOK+M0iUsymAXpt6hJwketi7Bzue34sfP+mFYO29a1RIpIcdmzSncaXqdGvVjiuXLxv0sHNHmlWvTIfGP2FmYcG+l348CTSYo+r1gp237uGeN99X18NGbZuz5v4pNDot/z69SUX7HIRFR6KKjOLR/Yc4CBMk/8hU9bB5h04su/GYgAiD+bBaq2PJ+ZvUbNyUHevW0SZfviS7kI1y5eLIrl1J6ihfvjwP5MHcCTC0c+nFA4pa5EUoZPF6WMuxApuWbUixDzvX7aCKfZUkeljFrjLbV2/7rPvzUWToYUpclSSpUuzETy9Jkj3wrQPcHpIkaaAkSR6SJNnFPb5xn/4TaLVanj17RkSEYWxkMONPyWEQxMXyNJBcg62t4yzVZCTsjMbpiB4zMxPGjx/P5s2b+fvv/bHhXmTo9YZJa6dOv8ROfuWJyso4fuwOPj4uHDwyjNevglgw9xC7d11i/qIOeHk5oNFouXXrFWq1jqAgFVGRWtRqQUy0npCQ0PhjBT/99BONm7Qml88QGtSbTaH8v7N65WU2bPjr829kBl+Vr26CK4QYBgwDkCSpIjBQCNFakqSpQDsMwZLbATtji+wC1kuSNB1wBbIDabK7UqvVdG/ViUpkY7h3Y7R6HQe2XmT046eM/zOpk4Vx0/9gcPe+nHx8Fzu5Ofdj3tCmd6cPehR8F5VKRWY3T+SWZrwKCTF8/RRGeHtm4aI27eftR4z6nW6Pn9D1+E58LOx5HBmMe8G8rJ47h+PHjrNw6FTaeVXF3tuaNxGBDO3YFxPzaIaXK0S2XKUIjlIxe/kiqv/ckI0nzxJ87wIaoaNU1cqMGvr1j1206dAWoRfMWLWJF74vKKspgb21FebGZgghCA2KIFIdES+gKeHj40PfyROYOWky6tBwNJKgbrOmtO/SmUO79mHhmnSHWSk3Qv/O4FqhUDBv7UImDh/HzpvH8I8JQW8r4eHqSdzWirmRBeFhyXdKANTRGhTypDs3xnIToqO+bgiCjOiWyQiXJKkPBu+wbWK9K373Yaa+th52a/kLpWNy0y9TS7R6HUc2nmXU49+YMH1SkrzjZ05iYLf+nHt5Axu5BY81r2jbr+On6aGrJ0bGJrwJDkJCQqaQk8UzM3c0IWmuZ8So3+n++Amdj+8mh5UdD8NDcC9k0MMTx46x9PdJdMtVFgdPS16HBTOySy+MTLT8VrEg2fIVJzhSxcyVC6nauDHLTp0m5OJN1Ho9JStV5bchQz7qmtKDVu3boReCMas38PLZC+pZ5MTR3gILY1OEEASHhKEKDP2gHvYe9wcTpkxCGx5KjJCo/XMz2nfuwpE9e7E0Thp1Q2lkhF6nS5KmUCiYs3oxk0aOZdPNs7yNDkVrIsfD1Z24AaulwjxVPVRaJp3XmMiNiY7I0MNvTBkMZ0CfYTDDPQukbmL05YmLe9YzUZoAvp33qv8AixcvZsiQIajV6vjYniVKlODFi23v+V68uxESZ1prML91dHRMIWazYSKaNas3v/32G+fPn2fp0qVERkaRdB9LhkajQaeL2z1NSFerdRQqMJxu3avi7m7Pjh2XkMlkTJ28h2VLjhEZqcHY2AIII/HEWK8XaDQaTp48Sbly5ZAkiXFjJ9Cjey/OnDmDk5MTpUuX/mom/xmkH9/TAO0PYLMkSZ2A58DPAEKIW5IkbQZuY7AR6Bm70/FB/jlylKwxthTNbDjUrJTLqJe5FNPP7cDX15dMmRLOz9jZ2bF002oeP35McHAwuXLlwszM7H1VvxcnJyeiTEBubUZWZ2dAIEkS6+7/S8VaaY9RJJPJWLxmFf7+/jx69IjMmTPj4uICwNIZ82nvVQ07U4O9u4uFPS46BVXdzMjmaA+ArZkpg8oVZuCunWz8+wA6nQ6ZTPbVvqRPnz5l24athAaGUKFWJSpWqki7X9rTtlM7Jk/6gxMzDlLRuAQxejWXwq/zIvolV6KuolCkvkhbomRJSuzcjkajwcjIKH7lvWy1ypw8eZvKWRLOcd32e0m2vIbXYWFhbN+ynfvXH5CrYE4mzPoDMzMzerTtiXioRy5PEMsrIZeo3KRyiu0XLlOI+4dukdMqX3za3fAbFK/9dUMQ/B+t5KeVNkBfYIoQ4lqsw43kQSd/HL6IHnpFOFDY1fCdUMpl1HItx7wzm1LUw+WbV6aLHkab6VFYmJLN2RkhDHq49fkRqtdpluZ6ZDIZi96jh8tnz6d77rLYmxk2vF2tbHHFiIqeFmRzMmyw2JqbMqRiQfrt2sHGfQe/iR5u37iF0KAQyteoTMVKFWnbsQNtOrRn8qRJXF66k9rG+YnWaTj59j6Pwt9y1u9e2vRw285kelimShWOXb5CdR+f+Lw3fX3JmjtBD3ds2c6Dm/fIWSAX42dMxszMjF7te6B5SxI9PO9/lYotUtbDQqULcvvfO+S1TViYuBlyi6KVM/TwG1Mr0fNoIcSH3St/YYQQKbvNzuCT2b17N/369ScqKs7cVrBu3Xrq1q2DmZlJrMOhuLOd77PAi9NACUmSYWpqmoIuCkxMjMmbNy89evRArzd4yH03TwLvfh8Fer2e8LBoZs/cT6PGjdBpJVq0KsFPDYty7+5rRv22DQ+PrDx69Iykk1rD5PfVq1dJasyUKRONGjVK9f5k8H3zTSegQohjGDy0IYQIBKq8J98EPiGkwsO798lskjyumqexAy9evEgy4Irjc13JS5LE4PG/Mb7vMCpb+2BvbMHFkKdosthSrUb1j67P0dExWWy4yKAw7LImPWwdqQnH08I9SZqZUoFcZ1iNSux44ktz5OBh5oycQXnTorgrLNl6cg078+9gxsKZyGQyJLXA2Epirf9WXqteU8I6O0VsXXG1NKF3qy4s3rIKLy+vVNt4d2DWrnMnuh9vj//9i+S1duFJeCCnot4wa/IyfH196dK0K7mi8uBh7MG1kzfZvOIvlv61hOETh9GjRS9yBufBySgTTzUPCfEMZGzHESm222dILzpd7EJQUACZ5B681j3jlf0jlg/8eh7uhQChzxhwJUYI8Rzon+h1JIbYdz8MX1oPH9x5gKfCOVm6u8Lpi+rh0Im/Mab3cMqa5MFWacW1iEeQwzzd9DAiOAT7zEmtrSPUkXhZ2iZJM1MqMPoWenjoMPN/+5PKlgXJqjRn55kV7Nq8nekLZhkmwVo9RuZyZt87xPMwf8q6eFHMyREXS2P6tuvEgo1rP1oP2/7yCz3btsPv+nUKODjwMCSE40FBzFixHF9fX3q06EwRspLD3JVHly/QdtVmFm1cxtDxw+ndugeFonxwNXbivuopbxxCGdhhXIrt9h78K50vdiYgIAB3hTsvNC94YvWUJQOWppj/S5ChhymS+IaYSpKU5B9ICPFh74rpjCRJCgyLguVjk44Bi4QQyR1wZJAmxo8fT1RUnFdbAAmVKobdu3ezadMmOnbsSGBgYKISCWcyDWc6k35vhNBz48YNg9dapTzWvFZgZmZKsWJFOXz4MFFRcSFZ9CQ4MAJzcyOsbcwoXDgzp07eJyZGS1RUnAVa3ORXQqPRc+rkSTp0KsfESU0BKF8+J4WLZKFa5SmYmZkmmlCDYfKqY+HChZQsWZLMmTOn1+3L4Bvzn96zzpE3N49Ub5OkCSF4Eu2X6j+xWq3m3LlznD9/PtYLWNqIiIjg1KlTKJRKFm5di1n9grwsYEWDUT2YtWR+klXlz8HK0ZaAqJAkaeYKS56EJTXXCo+OQa9QftXBllarZeaY6XTO1JSCDrnJau1FY7eaqG+E8e+//wKQs0Bu8rhnxdQGqjnlp5JLfgo5ZKeEfS5aO1ZkxrgpH92ulZUVK7ZspGCXZtz3sSVTs+qs3LEFNzc3Zk6cSTl1BUrZl8bdwoPSdmUoHlWSedPm4+XlxYb96yjcLz+6mlHUHV+TVVtXYm6essd6R0dHNu5bR6WRxZHqhlP1t1Js3LcOe3v7z7ltH41eSGl+/D8gSdJjSZKeJHo8liTpSex7e751/74HcubLxRONb5I0IQTP1W++sB4qWLJjNfbNchFUSsnPE39h9tJ01EN7O/wikjrjsVCa8zg0aWiV8OgYdEZfXw9nj/2TnpkbUtg5J9lsPWjuVRXd7eB4PcyRLy95vbKgsFBQJ3MOanrloIRrFsq7ZaVbtqLMmphyTM7UsLKyYtnmTeRu24brbq7Y16/H8m1bcXNzY/YfM6hlXIyKrsXIbO1GZdfiVJUVYMGMeXh5ebF27wZydC1KcFlB5eH1WbFldap6uH7vekoNKoOqagxlBpdj3Z71GXr47dkLPMEwyTse+3w/Bu+430oPFwBFgPmxjyKxaRl8Iu8766lWa2jatCkqlSpRuixR3jgHQXE7owk7pGq1FkmSqFmzBlmyeFKgQD5mzZpJdHR0bFimOIdFcZNKHWZmRrRoWZpnL2azY9cAXr2ZR8lS2ZDL4ya6cbujevR6QUhIEE1+TmolUaRIFmxtzXBwsMfYWJGoTwYDnxMnTlK0aNFYb74Z/Bf4nkxw052KlSqyfPYiTr26QYlMuVHrNOx7cZ485Yu81+vhubPnGD9gFFklV/QIxktvGTNrAoUKF0q1rS0b/2L1zKXkNPYgXEQTZK5i+tLZuLmla4QEALoN7M30/mNp7V6ZTBYOPAt9w1uFhm1vIsjy+i15MjnxNjyS2eeu07731z328fjxY1wke0yNkp7HLGCak38PHKdChQrUqFWT9YtW8Tz4FR2zV0ZCRogmAgtrS5wdXNhy58wnta1UKqlbvz5169dPkn7r0m06WpVJkpbLJjcrTy8DDIO1Vm1bprkdMzMzmjRtDE0/qZvpQobJWTKKpvJe2j/c/zAVK1Vk2azFnH17laKO+VDrNRx8c5q8FQulqodj+40mM+6G+KBGvoydPT5NerhqxjKyG3kRiYpgy0hmLPsyetilfx9mD/mdzjlK4WZlx5NgP17L9Gx9FU6WV37kdXXkbVgkM0/fpH2vgenefmo8fvyYTDJbTI2SnscsYpmdfw8ei9fDjUtX8DrAlz6liiFJEsHRUZhbW+GdyYWV5/Z9Utvv08M7V25S261FkrR8jj5MP23wXGtlZUXL2PA4acHMzIzGGXr4vXEC6CSEOAcgSVJJoJsQov037FMxIUSBRK+PSpKUEbjxMyhVqhQ7dux656ynYccwJkZgmCjG8b7viD7R+4YdUr1ej7OzMzt3GlwPCCHo3z/OwChuImuYuCoUCtRqLd17Vo0/AqBUGjF5agsqlR9HRETcjqmIr18IePzIj8KFE6yyw8KiCAmJ5NKlUwwePJjt27cjSYZwMIY+6AgMDCRbtmzs37+fwoULf9pNy+C74T+9A2pkZMTCdcvQlHNhyrOdzPM/RN72VRk5YXSK+SMjIxnf73e6OjaikWtVmrhW4xe7+vzWa1h8ENyUePDgAZtmrKK/V3MauFWgtXsN6suKMbTnlxnslCxdisFzxrFXeYdJz7bwr90Lpq9dwMzV69mvNaXnofPMfviWVsNGU7NO3S/Sh/dhbW1NmDa544wwTTh2ToZVcRMTExZuWI6RozkPVb7468KwcLTGOZMzMToNcuWH10WSH5J/P0oTJdG6pE4xVNooTC1M01xHBt83Qoig/7F31uFNXX8cfm+07i1tKe7u7s4Pd2cM9zGG24a7Oxsw3GU4w93dpWiB0tJST9LY/f2RWmgLLS2wjbzPk6ftyTnnnpsmnxz5Ssyv9gkeiwRByEry37zfFTKZjGUbliPUcmN+0GZWRu6leI8qjJk8Nsn6UVFRjP95LJ0dW9PIvS6NPf5HB9vmjO478pN6uHHWWvp5d6SBZ3Vae9bnf4YKDO8z5IvcV9ny5fhl9mS2aF4y6tZBDsjeM2PV78xZvYH9Glt6HbjKnAdBtBs+7pvoYZg+KlF5qDbSTA8Xr1uF4OLAg7B3BERHYePmTAZPT6L1OiSf8AOF1OmhXKlA/UFAvCidGmvb1Pv4WvjHUiF28QkgiuIF4OO7Rl8egyAIOWL/EAQhO7HHWxY+iwkTJmBra40gxObqjD0xjI0+mzDv58c0IjbdSnw064SbkhcuXIg5/YSEp5IAOp0evd5IhbJjGTE8PnuAk5MNRmPsohMSnsBGRGjo12cV+/ZeRxRFVKpofvl5I/Xr18PT05Nr1y7RpEkJDh4ezqo1vVAqZSgUckDKu3fBVKtWjdDQ0FS/Xhb+WfynT0DBtJs7ePRwGD38k3VPnTpFIUl2HBTxue6clA7klWTm3LlzVKtWLcl2e7ftoqp9EeTS+Jczq2NGhJeX8fPzI1OmTEm2SwslS5Wk5IaVicqnzFuQ7tdKDRkyZMA5lzu3Xz2kkIspJ1OkTsVZ7XWWtIoPfufs7MyoGePYMWEFnbP/D6lEgiiK7H11jvptmybb/9UrV1gweTqRQUGIchlNO7Sj/Y+d4nbekqLFj805Oucw9dwaIBEkGEUjR0IO02ZUyoOg/LP4rtIJpIiY1CUVgIThOnMCBTGZe1lMvTDp4ZDRQxky+tORsE+dOkU+MSf2inj/SielI7nErB/Vwz3bdlHJpjhySbweZnHIiOh//gvqYSlKrluVqPwfoYc5PLgZ8Jgi7rkAiNSqOBl1m0UtB8bVc3Z2ZsSUCeybtZh+OQrG6eHWh9ep3yr51DdXr1xh4dSpRAYFI8pkNOnQnvadPq6HzTq1Ys/ig7TMUjNOD/e8OUWrX9om2+afjUUPk+CRIAi/A7H5czoAD7/heMCUq/m4IAhPMa1EsgCdv+2Q/t0UKFCACxcuMGbMGC5cuIC3tzfXr1/HaOYTHZtKJeFJp4i1tRWjR49i5syZhISE8eFeQJcuXeJ+nz59OkajkfjFZKzZbmwEXdBojCxeeJgaNQtSs2ZBli4+ErMA/fCzaWobFBRB44azkMtlWFtbUadOLVasWM6atWvIm8+DLdsHxOnY8aNKpNLYfiTo9QY2btxI797/5jiDFv7zC9DUEB0djTyJl0SB7KO+T9GaaOykiXepFRJ5qnym/itMWziDkQOGcebBNeykNoTIwhk6cyTe3t5m9erUrcObl6+ZunozGZUu+EeHUP5/Vejco0uS/T558oRpvwzjl4IV8cjpQLRex+rNu1mt0/Fjj+7JjqdNhzYE+geyYtvvuEs9eGcIoP4P9WnaIvmF7j8ZEb4nX6aUUvjDKIuCIFwTRdFip/OZREdHIxcT66Fc/LiuRau1OEqS0EPh+9TDKQtmMmrAMI4/vom91IZgSSSDp49OpIe169bF3+8Vw9dvIrOtE6+jwihTuzqdunVNst8nT54wY/AQhpYsTYZCRdHodPy5fYdJD7snr4et27XhnX8gs3ZswFPhir82mLqtGtCkuUUP/0N0BHoB/TCtAM5g2oj7ZoiieFQQhFxAnpgxPRBFMeW56SwkQhRN0WVHjRpF0aJFkUqlFCxYkLt375MwiA+ISCRSRNGIKBpRKpX88cfvtG/fHg8PD3r06IEoxp5Qmk5KfX19yZ49OwaDgUOHDpHwhDS+nnm02qgoHaNGbGb576c4eOAagiBBItHHLIiNcWNJGAxJpxNxdLRi/frNyOVyzp45QctWJc020WRSCaXL5ODkiYeAgEqlxs/P78u8qBa+GpYFKPDu3TvW/7me6xev4/v6LuXci2KjMJlnRhu03NY9YXjZssm2r9mwDosPTyOfS/a4D02IJpxgaSS+vr4smjYHQRCo17Ix1apX/+ju9D8ZrVbLrr928/fuEzg62dOhayuKFUts1ePo6MiiVUsJCgoiMjKSzJkzJ5nuQBAEuvTsStsf2uHv74+Hhwd2dnaJ6sWyYflK2mUpiIedKQKwUibnxwJlGLlxMz9065psSgWJRMLPw36me7/uBAQE4Onp+VkpJdIDURQ5deoU+zbvwKDXU7tpQ2rVqZ26dBCiJe9dEhxIouzwVx/FfwCTHm7g+oVrPAq4T2mXEnF6qDVouWd8zJiP6GGtRrVZ8PcM8jjnSKCHYbyXR+Dr68vCKXMQJBLqt2pMterV/vV6eDhGD9t9RA8XplAPf+zRndYdO6RMD1esoGPuPGSwN51QW8nldC1anMGbNvFD14/r4U9DfqZrH4se/lcRRVEDzI15/CMQBKEvsF4UxVsxfzsLgtBVFMVvujD+t3Lr1i0aN27Mu3dBSCQCSqWSjRs3smzZMurUqUN0tA69Xo8gmHykjUaIDTxkNMLBgwdp3749CxcujLEgiI+kK4oiP//8M/fu3SMsLAy9Xk9KPVmuX3vB9WsvcHCwpmXrkmij9ezYfhm1WotMJmA0ShKc0Jp+ajTRHDp0iPr16+ORwTsmFYs5T58EErtotbOzpUKFConqWPh38d0vQF+9ekXvVr0poStBddvq6GVahp+aRdPcNZHKZVyOvk+Pkf1wcHBIto8SJUqQp25xlh7YSVFlDiJFNVd1vmQrmIN9k5dTy6swoiiyc/wSrpy7yNAxI7/iHaYPBoOB7j/0JfymHZkVZYnURzL4+BS6j2xBm/ZJR59wc3PDzc3tk31bW1unKN2D37PnNPDIa1amkMqwRkp0dDTW1h/36bS1tU1zWom0Mn/GLB7tP0MDn8JIJRIOz1zJ2eMnGT99Suo6suz4AyAIQg1RFI+Kojjsg/LKQOK8IhY+yqtXr+jVqjfFo0tR2aYm0VI9v12YQsMcdZDJZFwz3qHXqD6f1MP89YqyfP9WCstyEYmam8aHZCuYg13jVlHVrRggsmXM71w+e5Fhv474ejeYThgMBnr+0BfNDXtyKMqiNkQx/PgUuoxsQeuvpIevnj+nWWbz1IoKmQwbQWLRw+8cQRCOkcSKQRTFaoIg/C6KYo9vMKzuoiguSjCWEEEQuvONT2b/jWg0GqpXr05wcGhMiUBERCQNGjSgS5cu9OzZk3fv3vH06VPOnz//gYm6gE6nZ9u2baxdu5Y7d+6Q1OLywYMHiKKIo6Mjtra2aLXhmJ+Aguk0M+HvxriFbmioms2bLtC+QwUmTm7Fr6O3YjSKqNWJF7NarZZ3794B0L1bD6pUqUCduoWoUCE3er0Bf/9QgoMjACPW1tbkz5+PunXrpvFVtPCt+U8HIUoJi2YsooqxCsVciuGkdKJVjjY0z96W84qHZOtenN/3rKJhk4Yf7UMQBIb9OoJfV0/DtUN+CvatxsQl0wl94Efn3NXJ5OBOZkcPuuapyc1DZz/bdCA8PJwDBw5w8OBBIiIiPt0gHTl+/AQhtxUUsquLo8KdDDbZKGv1A0tnr0aj0Xy6g3SgYMni3Hhr/tpFRKvRK2VYWVkl08ocrVbLyZMn2b17N/7+/p9ukAJCQ0PZv38/hw4dSuCon5i3b99yYc9h+hasQVZnDzI5utElf2XeXLrDw4epc88RRSHFj7QgCEJLQRDuCoJgFAQh2UizgiDUFQThoSAIvoIgDE9Q7iIIwmFBEB7H/HRO8NyImPoPBUGo85lDXCkIgk1Mfx6CIAwRBOEWMBDY9pl9frcsmrGISvpqFHEujpPSmZbZ29Isezuu2twjd9/CLN/7Z8r08LcRjFs7Bc8fc1NsQCUmLZ3G+7uvaZ+1Nj72HvjYZ+CHbHW5fvD8v1YPI28rKGFXFyeFB17W2aim7MTvs9d8PT0sUYIbb8yTs4drNOjkcosefr96GMtgYFASD4DZaez7c5EICcwdBEGQAoq0dJjS/8e/FV9fXy5evPhBOhXYu3cvWq2OeP9OkwlAdHQ0S5YsY+HCxWzbtg1BEGJ8N2MDBxnj+jCVg5ubeV7lWJydnREEAalUytixY7GxsSLehFZEECQxfcb2G+tDGmvKK0EVpWft6tPUqVsIlUpL0WLZsbNTxI3XhIjBoKdyZVN62Hz58lGlSg3+V3samTP2x8O1F2/fhqJS6bC1tWHUqJGcOHEi3dJ4Wfh2fPcL0DtX75DTIadZWWHXwsh0ctr/0IEMGRInbk+O3Llz07lbF1q0asHTp08ppPQyMy8TBIFC1t7cvJn6yONHDh2hbc1WHBu7n6O/7aNNzVYcP3os1f18LudPXsZDzGNWJpXIcTD48OTJk68yhvadf2R/yEvOvnxMtF7P0/eBzLh+gl6Df0mRGd/Tp09pXrM5Wwdv4sy4k/Rq0JMl89IWm2b/nr38WK8F1+du4/yMjbSr04TzZ88lWffWrVsUsvNKNNZidt5cv3otxdcUAYMx5Y80cgdohimsf5LETCQWAf8D8gNtBUHIH/P0cOCoKIq5gKMxfxPzfBugAFAXWBzTT2qZCdwUBOEAcAnQAdVFUWwqiuLuz+jvu+bO1bvksM9lVlbIpQhS/WfqYfd4Pcwny5hID/PLfT5LD48ePkLHui24MGM756dvo0Od5hw/9vX08MLJy3gbza0xZBI5zvqMX00P2/34I7ve+nPm2VOi9XqeBL1jyoVz9Bw8KMV62LJWM3aOWM+lKUfp27g7S+al7TBq/559/FivJddm7+DctE20q23RQ76uHgIgiuK1pB4xzz343H7TyN/AFkEQagiCUB1TgKSDaezzk/+PfyP+/v6ULl2aggULUrFiJWxt7Shfvjy+vr4ABAYGotcnDBqU0LdSglarR61Wc+bMWeIj3EqI9cOUSiXUr18fgGHDhiZYXJr6EgSBxo0bo9VqAfjpp59YsGA+WbL4IJdLKVSoADt37qBEiRLIZFLkcmnMgjShKS+AgFwu4+SJ+9jYWPHyRTB58nphbS2LGYsRqVSM6SM+bsDp06dRqXS8eRNOeLg25lRVglarZdCgQZ+07rDw7+C7N8F1dHEkLDwMJ6VTXFmULgob+7T5xLi5uXFBVCUqf2eIwt096R2n5AgPD2fO6Jn0dO+AlcwKURQpqinA9OHTKHW89Ef9hNIL70we3DUmtstXE5Iis7L0wNXVlcUb1rLmj+VMvnABz4wZGTx/JkWLFv1kW1EUGdF3OE0lDcngbgovXkmswLo1myhftTxFihT5RA+JCQoKYvnUeYzK3xilzCSeNaILMW34WDYd3p3oFMLd3Z13+sTviQBdFPk8Uz6xN33XfB2TM1EU7wOfmtCWBnxFUXwaU3cT0Bi4F/Ozaky91ZgSow+LKd8UE4TimSAIvjH9pCoJrCiKCwRBWAW0BZwwTeLUgiBsFEXRkrE6lTi6OBAeGoajwimuLEofhY192r7w3dzceC8kPg0LFiM/Sw/n/zadwTmaYC1XIooipaPyMHvkZEod+Tp66JXJgyfGl4nKo4Svq4eL1q9jzR/L2XfpIhkyZuSXObNTrIcj+w2jtXUdPN1MelhVLMvK9dsoX7XCZ+vhH1PmMiJPkwR6WJipw8ey2aKHX0UPY0kQaTbRLQDChwHbvhLDgB5Ab0xjOwQsT0uHKfx//Oto2LAhN27cxGCINXEVOH/+PIULF+bSpUtUqVKF+KA+CQMCJTSH5YMyUz2pVCBDBncWLDBFCP/5558JCQlh1qxZGAwGU/A5uZQHDy+SJYsPmzZtpUqVKnTp0sUsMi5A48aNuX79Onfu3GHFihWcPHkqwZhM4xBFkS2bL9K4cSOiVFGool7Q/6daHD58h5D3UURGamjXoSITJo7j92Wmt4Np4Svw4VtYFMUYf1QL/wW++wXoD31+YOWIlbRwa4FcIkdn1HEg5ADtRqctd3358uVZIJvJo+BX5Hb1AeBB8EteK1WUKlUqVX2dPXuWAkIurGRW3Hv/kN2PD6IwKgjQBjKg5wCW/LkEhSJNliyfpGmLJqz7vROe2tw4KNwQRZGnqqvkKOOZqlORtOLu7s6gkan3GXv16hXS9xIyuMbntpIIEsooS7Jv+77PmnCdPHGCcnbZ4iZbAA5KGwpYZeDSpUtxJiWxFClShCBbkdsBLyiUIQsAvu/9uWd8z+iKFVNxZQFj6lJbugmCcCXB37+Lovh7ajr4BBmBhHaUr4AyMb9nEEXRH0AURX9BEDwStLnwQZuMn3NxURQjgN+B3wVByIcptP91QRDOi6LY4XP6/F7p2OcHVg77k6aureL08O/QfbQfk3Y9nK+chW+IHzmdTWlYHoa8xN824rP0sJhVFqzlSm4F+LL1zjGskBOoDmJg759YtGLpV9HDjb93wkebG8cYPXykukK2sv8ePZSHSvD0MtfDirbF2Z8WPbTNbqaH9kobCii8LHr4FfUwhn+cOaooikZgKbBUEAQXwEcUxa+WB1QQhB6YFsC4u7tz4sSJr3XpTxIZGRk3Ho1GQ/v27Wnbti3mCzDTovL06dOIosjEiROTyP/74QL0w8+FiEKhoGDBgjx+/JjHjx8DUL16dcqXL8+DB/fJlcsTUbRm8qQJhEeouXHjOgaDIdnAYBqNhvfv39O4cWMaNkzaPUMiEShYsBAAd+7cRiaT0umH6jg62uDt7YxRFHn8KIC///6bsLAwJk+eTFRU/OaUj09GZs6cho2NDVeuXEnyGl+ThP+vfwL/tPGklO9+AVq7bm1C34fy58I/sTHYoBJUtO7XmsbNGqepX6lUytyVSxg/dDRb714BRNxz+DBv0bLURfiLQUTkbVQAu+4foKNLS+ykdoRpw7l58z7Tx01j9KQxaRrvp3BxcWHRmmmM/mUCYW+1GIRoStUszLipU7/oddOTbx0oUSKRMGf5EsYPG8XWG7uRCgKOPp7M/fP3VE+YUxn1MUgUxY/5Kx0BPJN4apQoirtS0H9yO+3p3eaTxOyID43xu6qX1v6+N+rUrU1YcCirFi7H2mCDWqKiTf/00cN5qxYzbsgYdj09j4iIR86MLJix9PP0UBR5HfGOrbeO0DdrPRzkNoRoIrnw+BkzJkxl1IRf0zTeT+Hi4sL8NdMY88tEIt9q0QvRlKhViLH/Kj1M/HETzU4v0olkurPoYZrbJIsoiu8/t+2XQhCEE0AjTPPOG8A7QRBOiqL4yyfapfX/AUDMJsPvAHny5BGrVq2a0qZfnBMnThA7nlOnTjF27FjCw2MXYLE+nAmJT5liinIb+3x8ehOTT2bC1Ckgl0vp27c3/fv3TzSGCRMn8O7defr/3J7TJ4xUqmryO23aeBOurm506GC+l/vkyRN2797N5MmTCQ4OSeBfHXttE0qlkq5duzJw4C9ERUXRrFkTnvvNwcnJNqaGjrNnHzFx4iLCw6MAAUGQoFarUCqVREdrmT17JuPHT+DMmdMUKlQopS/rFyPh/+ufwD9tPCnlu1+AArRq14oWbVoQHh6Ovb19mpybX7x4wYoFf/Dg1gNy5M3BoN9GxOV7s7W1/UTrpKlQoQILxXkE+QVTzbYidlI7jKIRjaClbqZaLD38J+rR6i9uF58/f352HNxIeHg4CoUixYEu/gn4+PhgdDHyVhWAp43phMIoGrmguczoFp+3eK9arRrrZi+lir4gVjLThCk8WsXd6AB+LVMmyTZubm7MX7EMlUqF0Wj8LHNBEdI18booijXT2MUrIFOCv32ANzG/BwiC4BWz2+8FBKagTZqJ2W3fm179fU+0at+KFm3TTw+XL1jOg1v3yZE3J4PHDk8XPVwaPYcg32Dqu5fEQW6DURRRo6NJ7spMObod9civo4fbD2741+qh3knEPyoQL1vTIZxRNHI26jrDW/z2WX1WrVaNtbOWUVlfKE4PI6JV3I325zeLHn5VPRQEITZkacJFrCCKor0gCGdEUUzNEXN64SiKYrggCN2AP0VR/C0mYNxHSYf/x7+KYsWKxeRLjvXrNBLvVxn7e8KUKUYEQYjZhDFgvpdhJNaUVSqVYGtrzaBBg0iK8PAwXN0Su565u9sRHm7uzTJ16lTGjRuHwSCi00VjWujGEusHasTBwYEBAwbw228mTbG1taVJk0aMGrGN+Qs7IpVKiIzUMKD/OkJCItDpEo5fgigaad++Ld7e3jx54vvV3BssfB2++yBEsUgkEpycnNI02Xry5Al9W/XC7ZItPypbkPmWG4PaD+Dp06efPdkCcHBw4JdJQ7gcdhMlCsJ1EbzTv8czoycymRQHqd1XjQLp4ODwr5psgclHZMqiqewS93Eg6G9OBpzmj8A/qf5jDQoXLvxZfbq6utJj5EAm39vNricX2fHkPDMeH2D41HEolcqPtrWxsUmTr9rXivqYQi4DuQRByCYIggKTH2ZsAKDdQKeY3zsBuxKUtxEEQSkIQjYgF6YgQhb+AaSfHvbG7YIdneStyHTDnV/a/Zwuejhg/DDOBt7DWpATGh1FQHQont6eSGVSnOS2Fj38BIIgMHnhNLZqDrHn7VGOvTnHolfrqdyhZpr0sOeogUx9uIvdTy+w8+l5Zj7Zx4hpFj3kK+uhKIoOoijax/yMfdjHPPctFp8AsphFdyssm4PJYm9vz8SJE5HJBOIXnAn3Ej58DwsxprgC8See8aeREgm4uTnTqlULrl69io+PT5LXrVvnf2zacBmNRhtXFhwcwZ7d16hdu3Zc2ebNmxk1ahQajSZm8ZnUmKBEiZL4+fmRO3dufv/9d54+fQrA/PmL8X2sJVf2ITRqMI8cWQcRHa3AYDB+0I+AUmlFo0aNyJAhg2Xx+R/ku12Avnv3jg3r1rN6xZ+8fJk4mMTnsHjGIhpY1ySvcy5kEhk5HLPR3KE+8yfNS3PfNWrVYNDEwby0e4u9txPZc+fA3sGeKJ2KKIX6ox/OR48esWLZCrZu3ppoJyulnDlzhp+6/kKPdn34a/tfGAxfzXXDjPDwcP5YvIw+HTozfvjoOB+GlJA9e3a2H9lOi+mtsfqfNR65nHl89y4njh9Pwpfi09y8eZOzR4/jnsWTiCKulBzQko2HdlG2fLlU95VaRDHlj7QgCEJTQRBeAeWAfYIg/B1T7i0Iwn7TWEQ90A9TlMP7wBZRFO/GdDEVqCUIwmOgVszfxDy/BVNgjoNA36/pD2TBnFg9XJXOeljfqiZ5EuhhM/v00cPqNWvw87hhPJYHY+fpTLZcObBzsCdSqyZcGv1pPfw97Xo4oNtAerX/9nq4fMlS+nbszPgRqdfDrYd30HhSOyQ1HHDJ5sLjO/c4cfzEZ+vhmSPHcMvkSWRRF0oNbM7GwxY95BvooSAIMkEQiguCUCXB47wgCFUFQcjyuf2mkfGYXhNfURQvC4KQHUj5GzYJkvt//Nv55Zdf2LlzZ8xfsQvR5JFKpQk2CxMu4qQoFFb89NNPbNiw4aN5f6tXr06JEuWoWH4S74IimDf3AEULjyRLlmw4OjoCcPfuXTp27BiX69P8ZDYWEWtrK0qXLoW3tze9e/dl0KAhFChQkJEjR+Ls7MyRIyfYs+cQPXuM5sqVG1SuVCUuLcyHWIIO/Xf5LhegRw4dpluD9rxYcYnAdbcY1Ko3a1asTnO/T+4/JptDZrMyL9sMBPilT361Nu3b8j5bONeibxGqC+VhqC9r323m519/SdKPShRFpk+YyZAWY7g3z58T46/Tolpbrl65mqrrLpm3lFk9l5LhSgFy3S/Pjl+PMqD7L581SUkLYWFhdG/Vgai912kty0uux1pG/tiXC+dSHihQJpNxcOdeos89paU0L9XDPdjy2wLmz5iTqrHs3rmTOT8NoUIodHPOQYbHAexYsx6Z7CtYtadispXWf5EoijtFUfQRRVEpimIGURTrxJS/EUWxXoJ6+0VRzC2KYg5RFCclKA8WRbGGKIq5Yn6+T/DcpJj6eURRPJC2kVr4XI78fZgu9TvwZNlV/FfdY2CLPumih773fMlqbz7X9bL1JODl2zT3DdC6fVv8Mug4F/qA4Ohw7r57xqIn++g/elCyejhj4gxGthnF86V+XJh2mdY12nyWHs7rvYTMN/NT4Ek59ow9ws89vpEetm6P5uBV2lnnIO8zFaM69/ksPVSfeUYzClA5xItNYxamWg937djJzL7DKRWgoKNNflzuBbN99QaLHn47PTwIzMI8B2jemJ/l09j3ZyGK4lZRFAuLotgn5u+noig2T2OfSf4//gs0aNCAPHnyEG+KG2t6ayD+NFREEIyUKVMGHx9vzPNymk5DNRpNigL3CILAmjUbMBpseeX3nlEjtuH/JoybN+9SqlRpVCoVEydORK9PeCobO6b4PKN2dtbkz5+fdevWERWlJjJShVodjUajZf78+Zw8eRKAwoUL06hRI7JmzUrr1q2xtbVOcF+me9PptNSp85/5l1r4gO9uAapSqZg/diY/Z2tB1YzFqZixKAOyt2DX75t4/fr1pzuIITo6midPnpjtoLtlcCdIY+77H66NwNYxfdICWFtbs2LLSgr1Kc5Zz6uElI9i2oaZVKtRLcn6N27c4MKW6zS160xh51KUdqpCI2knfvt5fLK7TR8SEhLCX3/up55dezytfXBRulPFvgHvrkRw7VrKc7WlBxvXrKOiNCM1sxbG1caeQhmy8lPumsybOC3Fk7979+7x+vI9qnjmw8PWiUyO7vTIV5Nzuw8RGBj46Q4AnU7HqnmLGFaiBnncvXC3c6BpnqJkV8HfB5KeN2g0mkTvl7TwDzM5s/AvRaVSMW/sLPr5tKWyVynKexWnb5a27Fy2Je166OmWtB46fb75bUKsra35Y+Mqsv1Qkf3KR7woKGHiqrlUq149yfo3btzgyvZrtHPtSHG3ElR0q0Qb2/aM+2VcqvRw9+p9NHZuh5eND65KN2q41Of9tfBvoodVFF7UzFYQVxt7CntmYWCBqsybNDVVevjq4gMquRfAw8aZTA4edMtdm7O7DqdSDxczqFAdcrtmxN3WgUY5SpIlXMpBix5+K1xFUawmimKj2AfwTBTFhqIobvyaAxEE4dePPL5s9MR/ORMnTozJ0RlL7KLPAOgBA6Iocu7cOV68SJwmD0AqlaQoNRPA9evX8fV9gtFoRKMxABJ0Oj1v376lcuUq7NmzJ4lNHNMyQiIRaNOmNcuXL6dfv74Jxho/dpVKw6pVqxJdt2rVqrRr1w4bGysEQUQul2JtrWDx4sW4urqmaOwW/n18d0GIrl69Sl65D1ayeJ8UqURCKetcnDh2gvYd23+yj41r1rHp99VkUrrwVhNK4cqlGTH+V7r+3J1Z/afR1qMJ9go7onQqtgfuo/OkHuk2fhsbGzp06kCHTp/OLvH37sPkp6RZjix7uSP2Khd8fX3JnTv3J/u4f/8+3sbsSATzvYpMutxcPneFEiVKpP4mPpNrZy/SKYN5BDRnazuMERo0Gs0ng46Eh4czpPdAop8GscH/FAH697QrVpUS3rkoYOPFnTt3qJ7M5DUhfn5+ZLK2R/nB7n4pDx8unLtAw8bmEUPXrlzJX2vWkcXOgddRERSrUpkhY0Z/tn+dCBi+n4mUhS/I1atXyS3LbK6HgoQS8ryp0MP1bFy2Gh+FKwHRIRSpUpoR48fQ7efuzOo3jdbuzeL0cEfQXrpM6p5u47exsaF9p46079Txk3UP7zlMUXkxMz10UDjgFJo6PcxEYj3MYvgWeniBLl75zMqcre0QI1Onh5pnwWx+e4wAfQhtClenuFdu8ltlTJUeZlQ4opDKzcqLu2bh2pkLNPpQD1f8yc7V68ls48hrVTjFq1Vk6K8WPUxnViVRtuZrDyKGxAmAwQboBrgCE77ucP49tGjRAo1GQ+/evYmMjCL+xFEgYaRZEwkXfLGnoGAwiPTq1StF1zNton2Yf9NIdLSGq1evYe6TGotpRTplyhTs7OxixhqJTqfHPDiRyQpFo9Ekuq4gCCxbtoyuXbuye/dubGxsaNu27UdNhi38+/nuFqBKpRItiW3KtaIBpdXHAyUAnDl9msPLtjA6X1PkEimiKLLv6hUWzZ7Pz8MG0Xtqf5bOWIzmrRq5nYIuv3Wjdt3an+z3S2BlpSRETPxh16H7ZFCIWDw8PAiXBCcqD5UE45UpXxItvhwZMnrh7/ceR6v4ExS90YBG1Kfofkb+NJSq+gJk9XHBWWlHlF7D0ms78bZ3JVAfiYeHxyf7AFMKhkB14u/U1xGhZChqHsDj6JEjXNq4lZkVqyOLeb9svHWD5YsX0zOJUOgp5Stb+1n4j6JUKtGKSeghuhTq4Rn+XrqVkblaIJPIEEWR/ZcuxevhtP4snb4YdYAGhZ2CLr92/WZ6qLBSEm5MHJxIhzZVehgmJKGHQjBlMuVP8xhTg6ePN/7+ITgl0kNDqvXQSWlPlF7Dsps78bJ3JdAQnio9DIqOTFTuHxlCBh/z74ijh49wbt12JhWvG6eHW69c5Y9FS+j1U78UXS8pLHpoQhCEjqIorhVFcV6CMmugBdAMmPu1xySK4qwEY7EHBgBdgE2YzIT/kwQHB3P16lW8vLzSlDqkQ4cONGrUiPLlK/DixYuYhWhy+T9Np5bxZrEmM3svL68UXStbtmxIpQkDHsWa/yYMbhS78I29ppGyZcvi4+ND9+49UKk0H7SND4hka2tDu3ZJ55QWBIEyZcpQJpmI2Rb+e3x3JrjFixfnufCOd6qQuLIonZrL0Y+pWevT0b43r1hLi8xlkEtMOzuCIPC/zCU4tucgoihSo1YNth7azu4L+9hx9C8aNGrwxe7lUzRq2ZDbkvPojbq4Mn+1H1JPI5kzZ/5Iy3hy5syJfU4FDyNvxpl1vVW/4rXDQ2rXSXoiKYoiWzdupVWd1jSo2IjxI8cTFBSU5vtp27UT215fIzzalB/LYDSy/clF6jRvlGwuwbdv3zJ+xEiaVKnOmb8PIUpBJeqI1uuwlVlR3bEEG26fINJZRoECBVI0DicnJ3KVLMqBJ3fiXpOAyDD2Bz6jaetWZnW3rVrNDwWLIkvwfmmRrxCH/kpx+rIkEEBMxcOChWQoXrw4L6UBBKkT6qGKa/qHKdbDZt7lkUlMe5mCIFDXpxRHdyfQw8Pb2XNxLzuO7aRB42+ohy0acsV4GV0CPXwd9QrRXUyVHlpnV3Av7FbcZ99f9YoXth/Xw22bttLmf61oXLkBE0aljx626dKJLS9vmunh1kdXqN3s43o4bvgoGleuyemDhxPoodakh04l2Hz3GCrXVOphqSIcehH/mgRGhfJ3yCOatWlpVnfrqjW0z1XSTA+b5CzKoZ0WPUwnJgqC4A0QE4RoEXATKI4pMNI3QRAEF0EQJgK3MB18FBdFcZgoiimz8/4XIYoiv/32Gz4+PrRq1YqyZctRrFgx3r79uO/7x8zmHRwcuHHjOmvXrqFXrx7I5TLiF3bSDx7x7gSCIKVGjRopHnv16tXJkCFDjJVIbAqYhCei8SlW4v1NRW7evMXPP/+MSqVOUF8SV0cQRGxtralX73/Ur18/xeOx8N/muzsBlclkTF48k5F9hpA52BU5Eh7r/RkydTROTk6fbB8WGoaTs7lPp1QiQWI0CUiseVdCM69vRfbs2ek2phPLpi4loz47GokKtWsoC36fm6rxzV85h7FDJ7DlwkKkghz3HE4snjUvWROvedPncX3DHeo7tsBKbsX9g3fodr47G/atx8YmcZ6plJI/f356TxrBnMkzUUSLRBm11GragF4Dkv5eDQsLo3/HH2npkYPmRavha/uMAy+uEWKTncJCDvTqSLRGIy+VUWz/Y1OqXpNRkyYwa+IUhpzcj7VUjsTBljHzZ5EhQwbzMYSG4pLF3LRPIZVBGqJmiiIYLTv+FtIBmUzGlCUzGNl7CD4h7siQ8sT4hqHTRqVID0NDQnGyT0oPhX+kHnYe8SN/TP+DLGI21IKaCKdw5i1LpR6umMu4YRNYe3ERMkGOa1ZnFs6cn6wezp85j/tbr9POox7WLlbcOn2fnhe7sXb3hrTr4YSRzJwyE0W0kSijlppNGiZ7khgWFka/Dp1p6pSXhnlr4yt7xuHXtwixz0xhSQ5CNFFojQb8rCPZ/sfGVL0moyePZ+bEKYw+/hdWEjlSJxt+WzgzkR6GhoTi7GbuA2zSw5T54CaFRQ/NGAgcEwRBBByAn4CfvmWEcUEQZmA6ff0dKCSKYuLj8v8Qf/31F7NmzUKj0aHR6AGRO3fu0axZM86dO5eo/t9//42vry/VqlXHxcWZoUOHMmTIkESbSDKZjCZNmtCkSRNu377NuXPnY07+zVOXxEbNlUqlODg4sGDBgo+O12AwcPLkSYKDg6lQoQKnTp1i7969yOUSjMaYz5fZx1OCRCLQsFER9Hojx47eQ63WoFarMDe5NY1fEKBPn160aNGCKlWq/CO+Cyz8M/juFqAhISFcOneJ4uXK4+zlgEwuw+V1EPdv3yVn7pxxSdKTo0LNKlzYc5vqWYrGlfmFv8Mji3eyu87fkmYtm1KnXm1u3ryJnZ0dBQsWTPU4HRwcmL10BtHR0RgMho9OmiIjIzm09QidXHrG+UkVcCpMSMh79vy1l9btWiXbNjnevHnDnh27CA0KoVLtqmw6sAuVSoW1tfVHoyzu3LqNqg6elPTJhoiItUJO93ylGXnpCJWKlEQTEcV91VW6DuyNi4tLisejUqnYv3cfEolA697dqVajRrLmauWrVePklZvUypU3ruzxu0Ay5siR8hcgCSwmZxbSg1g9LFmxHI4ZHJHJZbi/CeL+nZTpYcVaVbnw112qZyoeV/YqPPAfq4dNWzaldjro4awlKdfDo9sO0d/7hzg9LOpagOCAUPbu2kOrtq1TfQ9xehj8nkq1qrFxfwr1cMs2Kln7UNw7u0kP5XI65yzPb7f2UaloSTQRKu6rXn2+HgoSWvfpSvWayethhRrVOHP2HtWzxpsr+wYHkDFHtpS/AElg0UMToijuAHYIglAL6AxMA0oKgrBSFMU0pTxJA4OAaGA0MCrBAkQARFEUHb7RuL4Is2fPJipKTbyBoYBeb+D69Ru8fPnSzNri9OnTNGvWjPHjxwMS3r8PY/z4CURERDBx4sRkr7Fr1y4KFSqEv39Aks9LpVIGDhzIsGHDPpqS6uHDh9SoUYPw8AgMBgMajZqsWbMyefJknj17hsFgoGDBgkRExPqfAogolVJmzelI1qzu9O+3ihV/nEKv12M0JjS5Nd27p6cHCxYssCw8LSTinzdD+II8evSINrU7cmnWMzS7Hfhz/Fr2TdqCy3mRsG3P6NWkC2dPn/loH+1//IGrynfsfnaRJ+/fcMrvNivfnGbwuFFf6S5Sj62tLeXLl6dw4cJpmhQqlcpP7ti/fv0adyFD4qBF8iw8vPkg1dc8e/oMvZt2JnLnE1zPR7N2yEKGDxiCra3tJ0P8P7p9m3yungAICGTw9uS9Nhx7qYRT98+z58URwoxvOLj8T7Zt2pyi8bx7944fm7bk+frd5HsWxstN++nfqQvBwYn9wgA6duvKkcgQtt27xaN3ARx89IAlj+7w86iRqXshPkBESPHDgoWkePToEe3rduD+4sfIDtmwdtJqDkzZhPtFPRE7ntC7aecU6GFHblgHsOfFeZ6EvObU61usCjzBkPFpe39/Sb62HnrK3BPpYTYrHx7e+jw97Nu8E9p9D8h4LZKNI+YyfMDgFOnhw9t3yONs8gWL1cNgXRh2gpRT98+z+/lhQnRv2b9sDds2plwPf2jSiocr/ibb3Wh8Vx+lb8duyerhD926cEIbyM7H1/ENfsvhp3dY/vIaA8dY9DA9EUXxsCiK7YCSgB+wXhCEj3+Yv9xYJKIoWouiaC+KokOCh/1/bfEJ8P79e0j0PhOQy2WEhISYlf72228xPpNC3EOl0jB37twkg/XE4urqyrp162Ii5JqnLpHJZNy7d48ZM2Z8dPEpiiL16tXjzZu3REREolJFYTSKPH36nFevXuHj40ODBg2YPHkyzs6O2NlZAXqsrCT8saIbWbO6A9CjZw0UCgkymQxrayUm81yT+a6NjZIZM2ZYFp8WkuS7WoCOGzyJKrpWFHWogM6oJY8sI60d/4cnLpTNUJReGVswffSUj4bkt7OzY8WWdRTq1YA72fXYNCrM8p0bko2gaDAYuHXrFrdu3UpxqP/0wM/Pj0uXLhEWFvbVrgng5eVFkBiYyJ/hje4VuQrlMiuLjo7m6tWrPHz4MK6+0Wjk1q1b3Lx5E51Ox4zRk+mbuSnlPAuT3y0HHbPWRXU9gLNnz35yLDny5efR+/gdQnt7e9x9PLkf/oaXVr6ULezOtHqN+LVMdTYtWZailABLZs2lmUt2muQuRoEMPjTNXYyGjplZNnd+kvWtra3pN2I4kvKlOOtghbxmJZZt3Zzm6G5fK++dhf8uE4ZOpKGsGaVdypn0UJGRdq518BJcKOdZhD6ZmjFj9ORP6uHKrWsp2rce93NpcGianxV/rbfoYQxeXl4E6IMS6eELzRtyFTB/jVKihzN/ncQvuRtQyacABT2y0TV3TbS336RID3Pmz8fj0Hg/NHt7ezwyZuBh5GueyZ5TMm9GJtZowfBiddiw6I8U6eHimXOpb52XBllLkc8tMw2zlqKOMhdL5iRt9mdtbU3/kcNQVCvGpQwSbOqX54/tmyx6+IUQRTFUFMXFoiiWBlIWCtVCmmjYsCFKpfyDUhGpVEr+/OaByu7fv09Si1Xgkz6j1apVo0uXLlhbK5DLJVhbK7C2VrBt29YURfS+ceMGgYGxc7XY6LZS4qPcCty+fZthw4YxZ84c5HLTPcnlUvr0+pM9u035k4ODI5BIBOrVq0fz5s3JmjUzjo52FC9elC1bttC+/acjqVv4PvluTHA1Gg3BfqG4KU0nYs8i7lDGPj9WElsiwsJxdXXBTm6Du8GRZ8+ekeMjJpJWVlY0bd6Mps2bffSaN2/e5Nf+o8mgc0NEJFARzKRFUyhYsGC63ltCVCoVP/Ueiu/NQJRGV6IkfrTqVI/+P/f+KrtQDg4OVGlcmUPb9lLVqRYKiZLHEQ94bH+f8U1Gx9U7uO8gCyfOI4vgjRoNamc9PQb3Yt642XjqnZEg8Cj6BZ44YqcwP2Uo6ZCHkwePUalSpY+OpVnrlnTfth2vt44UzJCRMHUUM47txtlajo+VgV03zxMR/I6SXpkpZOPE1atXqVYt6Zyqsdy6dIU2xWuZlZX2yc5f5w4nqnv08BEWTpxKHhs3ogxa3itFOvfrmyrztqQQ+f4mUhbSF41GQ8irUDycTD56j0PvUdI+L9YSG8LDwnFxdcFOYYO7mFI9bErT5k0/es2bN28ydsAovI3OiIj4S0OZsGDqF9fDgb2H8vxGELaiOyGSFzTrVJd+X1EPKzWswl97DvE/j6oopQruh/hyR+HL8Cbx2ScO7j/IoolzyCbzQmWMRuVkoPug3iyYMAtvgyMCAg80fmSU2GOvMPc1LeuSi1N/p0wPu23ZgVegI/ndMxGmjmL26b9wkivxlArsvn2OsKAgimfISgErlxTp4c2L12iUu5FZWQnPnEw4uztR3aOHjzB//HRyKT2IMmgJs9XTpX8fix5+JURRvPOtx/A9MGTIEDZs2EBQUDBqtQaJRIKVlZIlS5bELeJiKViwIG/fHvugB5PvvKen50evIwgCCxYsoEePHhw4cABbW1tatmz5yejVDx48YNKkSZw8eRKNJhrzAEMJMQURUqk0dO/eHZ3OAEiJiDD5tbZrs4gLl8czZNAGrKzsOHz4MFFRahQKBRKJwODBgy0Bhyx8lO9mASqTydCjjwuMoZRZE2WIwigakErjD4KjjGrs7Ow+0lPKUKvVjOo1go4OLXBycgTgvSaE4T2HsuP4XygUijRfIykmjp1OwCVn8juYIjKKopHty7dRoHBeatT4dE639GDwqEGs91nP9jUbiFZHU7RCEZaP/D3udX316hVLxs6nt2e7uPyDz8L86NWqO5NK/4KbjWlC8jz8Fcuur8GY02hmKhepV+Pg9OkUAc7OzsxdtYLFM2ez9sphnj97SrVMnvxSIg/uNrZoDUZ+PXuBgm4ZePTyOXVTEBBEplSg0emwUcSnOVDptFh9EIDE39+fpeOnMqZwbWzkMfcYEsiIvj+zdte2NE9+LUE3LKQFkx7qzPQwUq/CKBrN8jFGGdJPD8f0GUaPDA1xtjJZ3QWrQxnZewjbju76Yno4eex0Ii95UN6uIQBG0cj+PzaR/yvq4S8jB7Mh43r+XL+DaHU0hUsXZdnw5WZ6uHTcPAZkbh2nh09D/ejTuhvTK/bH3dY5puwVi69swJjfXA8jtGrsnT6drN3Z2Zl5a5azaMZsNt7ax7Nnz6jknoneuYvhbm2HzqBn0s0TFHTx5NHL5zRIgR7KlQqi9Tqs5fF6qNZpsbJJrIeLf5vO8NwN4+q+CAtgWO+BrN+z1aKHFv4zuLq6cuvWLZYuXcqBAwfInDkzAwYMoGTJkonqjh8/PiYwUWzKErCxsWLw4MFYWVmZ1Q0NDeXBgwdkzpzZzDe/UKFCKU7zcvPmTSpWrIhabfJfjz/5TIr4D5Veb/ignoBGY6Bsqd+oUKECjx5eIipKA0jQak0L1B9++IE6deqkeYPJwn+X78YEVyaTUaF2GW5FnQegoFM5joVe5b0hCBc30wfkfsgTHLK5Jorc9zmcPn2aPMZsOCkd48pcrJzJaciSZCS09MBoNHLyyAV87OOFThAkZJZXY+3yLV/kmkkhkUjo2LkjO49vZ/+FvUyeN8nsNd2zYzfllMXiJlsAbhInMpEBbYIUCVkdfEAq4cqr+I1btT6a4+E3aNK6eYrG4uPjw+S5s1m4YQ3F8mSnS6kSROl1BKgiCVRHUNHHg5lXT/MoLLGZXFI0bNOSrY+ux9UVRZEtD6/RoK15cKUDu/dSwzVH3OITIJuzB45qI48fpzEWREzUx5Q+LFj4EJlMRrmaZbkSehGAEm5lOBpyjWBdcJwe3nv/BPusbummhwWkmeMWnwCu1k7kFTJ+UT08ffgSuWxLx5VJBAl5ZTVZv3zrF7lmUkgkEjp07si2IzvZc3Y/k+ZMNntN9+7YTSWbwmZ66C51IrPEw0wPszv5IEqkXHxxL65MrYvmUMgdmrRKuR5OmTebRRtXUzRHTjoUKYvKoCVQHUGgJpKy7j7MvX0c34iAFOlho3bN2fn8opke7nx+gUZtW5jV2797L1Uc8pgtVLM4ZsAhCoseWvjP4eTkxPDhwzl58iRr165NcvEJUK5cOfbs2RMTQMwUsGfy5En06dOHNWvWsHbtWoKDgxk2bBheXl7UrVuXHDly0KRJE1QqVarHNXjwYCIjVRgMIh/mC4333YT4FCwSQEzSV14URZo1a4m7ewaiolR8GHxIrzfQsmXLRO0sWIjluzkBBRg+dggjQ8ew69LvOOKK2lXPWvEghcPzEBEShU0WJ6YtmJku19JoNCjExLv6SlGBWq1Ol2t8iCiKYJQk2k2WS62IVH2Za34OqogorCXmidKNRhEbwcpswgVQ0acMh4RbXHnxBAepDa+Mwfw0YVCK8/bFolarsZHJERFBAGuZFDe5FcH2GmwUErQIREdHf7Kfth07MNvvFaMPHySrnTPPIkMoV7cmrdq2Mb/HqCgcZB/6gYC1VJ7m/79pr9Ti1G8hbQz9bSijw0az9vJKnCUuqJwN/Kk7ROGQPEQEqbDO7Mi0BemTJ16j0WAlJv48KMW0fx6SQxRFBDGxHiqk1jH56v4ZREVEYS01P+0wGo3YSJRoDeZ6WDlLSfbqH3Dh4Qsc5Nb46ULoN3bwZ+thjBxiJZXhorDmvY0DVlIpOmnK9fDNy9dM/Hsnma1deaEOpkK9GrRqZ66H6sgobKSJvw+tJWn/PrTooYV/M9WrV0cikaDTmT7rq1evJnPmzMhi5g8ajRqpVEp0tD4urcvffx+id+/erF69OlXXunDhAuYLxdiFpREHB3uioqJiTkYhYTqX+M2o2LYidnY2tG7dim3btiVzNZHTp08TFBT00WBIFr5fvqsFqLW1NXOWzSQwMJCgoCCyZcuGTCbD19cXBwcHMmbMmG7XKlu2LMvFZVQylkMak3RbZ9Rxz/iYxo6tGTdsPOGh4VRvWI06det8MoLhxzAajRw7dpwD2w6hUYfyJOosOVwqxD3/WnWFdk1rfaSHr0uN+rWYtWsKBcQ8GI1GQt6HEBQWxB3VY3oo2sXVMxgNPBb8WLVtHTqdjvDwcLy9vdm/ey9Deg3AK7MPLTu2wdvbm0MH/+bU34extbenabvWiZKoZ8mSBX+9nlC1BmuZDCcrK0REjvu9pnORQqy69wAHh08H5JNIJAwePZKw/n15/fo1Pj4+SbarVqcWc3YfoVTGnHET4MhoDU80IYkCEXwOlo18C2nF2tqaWUtmfTU9/FO7hKrGUshi9dCg55buGf9zdGTCiHGEh4ZTtX71dNHD48eOc2D7IaLU77kbcZoCLvH+kU/UF2nUtGaa7ym9qFG/FnP3TqSQWy4zPbwd+YS+Sqe4enqjgYfGN/y5JbEeDu41AO8P9PDkQZMeNmuftB4GGLWERauxkspxUlpjFEVOBz6nQ94SbHh6M8V6OGTMCMJ+6vNxPaxbi5k7R1LCK3e8HmrVPNMFW/TQgoUYXr58Sa9evdFodIA+plSPXp/QVFZAo9GyefNmli5dmmz+4aRwdXUlMvLVB6UCNjY2BAUFcePGDebOnRvjhmE6+ZRKZWi1sb6ips+uVCpBqVSiUqno2LEja9eui1mkxi9QAaysrHn+/LllAWohSb6rBWgsHh4eZo7a+fLl+yLXaNOvPX8sWEtJeRFERK7obpK/UkGm9J5JWWllfGRe7Lywn71b97PozwWflRJAFEVGDBzN88NB5JOVpgYZOfBmE4GRd8niXIFIyVMyFDLQ9jPyb34pihQpQv7/FWH5nk14vXfBaNRxXXOHQp6Z+e38bJrmrINSoeSi5jYt+rSN+185OjrSvfWP5Ne4UcEpO/4vgui/pxsOGZ3IFClSzSsHkUGRTOv9C01/6k7TFvFmYBKJhGETJzG0axfqujjibWPD6VdvkMvkZLCxpXT27IlCpH8MR0dHHB0dk32+QIEC5K9TiZl/H6aSa1ai9FqOBj9l4MQxiQIRfA6WCZeF9OJr6WHLvh1YsGgD5W3yY0TkvOoe+SoUZs6AKdR0LEN2eUaOTN7Bge17mb9i0Wfr4ciBo/E79o7C8tLUk3ux+/Vm/FS3yOdYhSDZYxwL62jzD9PDPHWKsWTfVjKFOWE06LisukdBz8yMOLWAlnlrolQoORt5j+a92ifSw3wqdyo6Zcf/WbBJD72d8Q6VUMEjN1FvNEzuMZgWP3elaUtzPRw+ZQKDf+xOdWsPPJV2nHv3AoVUhruVPSWy5Ep3PSxYrwLz9u+jvGNOogxaToU/5JfJoy16aMFCDFu2bEkilyZJ/G0iIiIiVQvQoUOHMmTI0ASpX0SsrZV069YNuVxOqVKlWL9+PSdOnCBjxoy8fu2PVmsKPmQKjmT6pBkMBoKDg+nQoQPW1tbY2dkSERGB+QJUgk6n/WgAOwvfN9/lAjSlvH37llu3buHm5kbRokVTPSFq16kd5auU5/D+QwDMqPUjP7X/mXYOPZBLTF+6XtYZOXDzL06fPk2VKlVS1G9AQAA3b97E1dUVqVTKo2OvqGPfwfSkNfSwH8XaoKmUbC1QuVpHKlas+I9KCi8IAsPHjmBg4E+8O/WQvK6ZGJu5A85W9lx+84CtoVdp1aYtM+v1JGvWrHHtNq3bQBFNBmpkMvlTZLR3RxWt5sqdC/xYr21cvTzuXoyZv5S69eubiXOJkiUZOXsOC4YOobSVLTXzFySPhxuOTk48C7hAp1zmaWLSyi8jhnGvcUNOHjmGvb0dS+tP+mSEupRi8WWy8LVJsx7+0J7ylStweP/fAEyt1ZNfOv3ET5nbopCa9NDH3ostDw+mSQ+fnPCjkWNM6H8b6F9oJL+/nU6Otnp+rNbun6mHv41kYOBP+J++T163TIzL3AFnazsuvX7I5uDrtGrTlun1+pnr4doNFFZ7xumht707Kq2aizcv075Wx7h6uV0zMn7eMuo2SKyHo+fNZO7AERS3taJ67sLkcsmAo5MTfrdekCud9XDQyKHca9KAk4eP4epgx7L6v1r00IKFBGg0mgQmsLGYFormi1ARd3d33N3dU9V/7969efnyJfPnz0cuV6DVRtOyZQtmzJiRaByhoaGYR8gVEEUjJt9QKWBAr9cnsfAUAAk2NtZ06dIZZ2fnVI3RwveDZQGaBKIoMm/6fI5sOkEWfU4iZKGoM4SzaM2CVH9hZs2ale59egBw9+5dPAzecYvPWHIJ+Th37MInJ1yiKDJ/5kIOrDtKRm1uVPIwHmtvUFrfABIELJRL5eSzL0m1mpWoUKFC8h1+QwRB4JXvc0aUbW6WpL2Ud172h9+he5+eidpcPHaWVu7mzvz+EYGUdvbBKBrj+lFIZeS0c+Hx48cULlzYrH716tXZXqgwjho1JXLnRG8wsPbWbTIVL06mTJnS/T7z58+fLiZmCYmPl2fBwpdHFEXmz5jHya3HyCnJSqgkgnDnKOavWviZemj6bN+9excfqUfc4jOWQjY5uXD8XIr0cMHMhfy94ShZ9bmIkIVxX3OTSmJ9sI+vJ5fKKeRYnKo1K/6j9fD142eMKt/MTA9LZ8zDvtC7SerhheNnae1eyqzsdUQgxR0yYzTGR8qVS2XksHZLVg+3FS2AY5iRYtnyoDMa2PToKllKFbXooQULX5lGjRoxefIU1OoP06MYTZHL9XoEQcDa2oqlS5emOnq0IAhMnTqVkSNH8uzZM3x8fHB1TRxBWxCEjwQhE4DYRXJszlBTICOFQonRaMTV1YU6depw7do1smTJSqVKFRk7diw5c+ZM1Xgt/Lf552wD/4M4c+YM59ZdobVdN8o7V6eOfTOKBlZk9MBf09Svs7MzESROhB5mDMXdyw2j0YharU7yg280Gjl16hTHV12isbIXpR1qUtW6OdlVxXjxzjeuniiKaI3RREpCUhX++mPX/lI4OTsRrDZPdq7WRyNVJr0v4prBjWB1qFmZndKWwOhIBMH8rRykiUry/gVBYM6yZVhVq86Y6zeZeO8BOVq24tcpU9J2MykkvV7nr5V4XRCEloIg3BUEwSgIQtKh/Ez1nARB2CYIwgNBEO4LglAuwXP9BUF4GNPP9Jiy9oIg3EjwMAqCUDRto7XwJThz5gxXt1yku2dHamSoQnP3BlSKKsWvg8akqV9nZ2dC9eGJyt9rw3Hzck+RHp5ee5E2dt1jdLopeaOL8vxdfFRVURTRGqIJF0L/+Xro4kywKsKsTK3TIk2U1N6EWwY3glShZmUOCluCk9DDYF3yejj3j6U4/q8CU3zPMeflVfJ1aMJvUyen7WZSiEUPLXpoIZ7ChQvTu3cvbGysEASTyauNjRX9+vWjR49uFClSiObNmzJ37lwWLFhArly5+OGHH1IdSdrBwYEiRYokufgEUCqVZM6cGfP17YdbPVLiF6BSQECn0xIcHMTYsWPZtm0b585d4OXLV2zatIUSJUrg6+uLBQuxWE5Ak+Cv9bsppaxkthOd1S4n5x8cJTIy8rPz4nl7e+Oez5W7d29RwMG0Ex2mDeW2/Aqu7+2pXKY+6BXYOkkZOXYgVapUQq/XM3vafA7sOMLbF8HINHJeZXlCZjuTeVRFj3rMDhpKWVV1QnVBnHyzD61OR6QymBOHT5EnT55PmpttWLuJlYvWI0TLkdgY6DukO42aNPise0wN7Xt2Zt2v8+mZuw5yqQyjaGT7s3M0+TFp/6x23TsxuccI+to3xEqmRBRFVIKOc5H+NIwKx93WFPzi0uunWPl44OPjk2Q/SqWSrj170rVn4lOFL4XRaGTp/AUc3rkLa4kUbK0ZMHokZcqW/az+vuKO/x2gGbDsE/XmAQdFUWwhCIKCmDN5QRCqAY2BwqIoRguC4AEgiuJ6YH1MnULALlEUb3yZW7CQFvZs3EVl+3JmepjTMTtHHp1Osx465/bgut89irmZTsVCNGGc196mUnAGapVuiNSgROkkYcjYAVSO0cM50+ZzcMcRAl4GI4+W8yLHU7LZm3bWq3rVZVLgcCqoqvNeG8RhvwNE63REKFKnhysWrYNoBVIbA/2+kh6269GZ9WPn0TtvrTg93PrsHI07Jp3KIFYP+9g3itdDiZZz2pfUU4fhZmPyybzi74t1ZreP62GvHnTt1eOL3duHmPRwIYd27MJakCLYWTNgzAiLHmLRw++Vo0ePsmDBAt69e0fXrl3Q6XQolUrat29PqVLxlg7btm2jU6dOqFSmKNXPnr3gr7/+4sKFC+lqXbBjxw6qVKmCRhONTqdHEECr1WIwxAZHSspPVUQqlTJs2LCY8Zm01mAQiYpSM3bsWNatW5duY7Tw78ayAE0CvU6PRJAmKpcgwWg0JtEi5cxYPI0xg35l3bXzKAVrjI46ipYoxt/rH5HbpisShYzo8AiG95vOis2ubN+4i9tbg6lq25/Xwhs0BjV7nm+lXe7uOCvcUUqtyJIlC8dt1vP61nuqCm2xt3HAK1MGTiw5iEz2J937dE12PH/t2MW6yfupaN0DuVSBVqVhwci1OLk4UrlypWTbpQfValQn8G0Ak5b+iZvMnmBdBLWaN6B9p45J1i9cuDA/junHrOnzccaGEF0kZetUZl6DAcwcMw6baCMqvRavfDmZOn3eFx17alk0Zy5hJy4wo1xNZBIpwapIZgwbxcQVyz7LLCVt78KUI4rifeCjpj6CIDgAlYEfY9poAW3M072BqaIoRsc8F5hEF22Bjek2aAvpil6nR5rEok2CkGY9nLpgBmOHjOHUzXVYS6zQ2hkpVLkEF9Y+o4ZNX6RSGaqQcMb1ncWCLa7s2LiLB1vf8z/bn3jDG6L1KnY+3kanAl1xVbphJbUia+Ys7Jdv4NX9ECoKbbG3dsAzUwaOLTmIVPYnPT6ihzt37GLVlP2UtO6BTKZAq9Yw9yvq4bu3AUxY9iduMluCtVExevhDkvVj9XD29Pk4iTaE6iMpW7sy8xsOYMbo8VipQW2Ixjt/DqbNmPtFx55aFs2ey/tjl5lS/H/IJFLeqyKZPWQ0k/5catFDix7+pwgPD2ft2rVcuXKFIkWK0KlTp0R+kQEBAXTp0jUuPdT16zfIkiUzV65cwdbWNq6e0Wikf//+MYs703vQYBCJjFQxcuRI/vrrr3Qbd758+fDz82Pv3r34+/tToUIFIiMjadCgAeHh4ST2S4XMmTPz5s2bmO8F8+cMBiNnzpxJt/FZ+PcjfE0To69JyZIlxStXrnxW2yOHj7Bq0CbqOjaL+6J5o/LjTpbzrN7+Z7qMLzw8HI1Gg5ubG5XL1Cc3XZFI4vcD3que413+JXcvPaKapB+CIBAREUHgy/eE8xqJayC1MjYlQP2KB1lP4JnRE9nxrGS0yRYTQhv0Rh37pIs5dHFvsl+Yjaq3pEhIW6yk8SIXrg3mdc6jrNuxIl3u9VPodDqCg4NxdnZGqVR+sr7RaCQwMBBHR8e4oBqiKPLu3TusrKw+mj7g4sWLbP1zLaHvQ6hYqzqt2rfFxsYm2frpgV6vp1WN2swsX8vsFOnGGz/u+jgzauIEAARBuCqKYrJmXbFkss0t/pJ/YYqv/8uVOinq92MIgnACGCyKYqIPVYyp2O/APaAIcBUYIIpilCAIN4BdQF1AE9PH5Q/aPwEai6J4Jy1jtJA8adXDzSPX0tyzYZyO+EW85pzbNVZuXZUu40uoh7XKNKSGsS9SIV4P/VVPESv5cvfSA/4n6R+nh0F+wYSIrxEzvKVe5ib4q15xI+NpPLw94XhWvD/Qw7+lizn8ET1sUL0leULaYCWL18MIbTDBOY6x/j+qh1tWrjPpYe1qtP5Ketiyeh2mlqhnpoc3377gYXYHRk0cD1j00KKHX448efKIDx8+/OLXefnyJaVKlSIqSkVUlApra2usrZVcuHCBXLlyERAQwPbt29FoNAwaNJSEwXxsbKyYOnUK/fv3j+svICCArFmzxqRpMQ9K5ObmzLt379Jl3CdOnKBq1apJPqfT6ShdujS3bt3GtP8Ye/IJhw4dolixYnh5eREdrf9gjEYqVCj3WYvQj43nW2AZz8dJqXZbfECToHqN6mT/nw87ItdwJegcJ8IOcMpmHxPnjk+3azg4OODh4YFer0fUy8wWnwB2Sg+ePX2JNY5xkyV7e3usnRQgyngSfp9zkQe44LCLKQsm8OalPxlsMsZNtgBkEjlopUlEVYtHFRFttvgEsJe7EBQQnG73+inkcjmenp4pmmyBKYWAp6enWURHQRDw8PD46GRr28bNLB0yjhpRznR1KkTwX2fp1eFHtFptsm3SA5VKhb1MYTbZAvBxdMbf78OcXCkjlT5PboIgXEnwMLO1EwThiCAId5J4NE7hcGRAcWCJKIrFgChgeILnnIGywBBgi5Bg9i8IQhlAZZls/XOpXqM6GWtkZXXAZs75X2J/4GH2ikcYN3tCul0joR4KOpnZ4hPAWZmB509fYo2TmR5aOSoRRDmPQh9wMuwgJ6z3Mmn+BF6/9Mfjs/RQY7b4BLCTu/DuP6mHW1g0cCIVgjxpryjF282X6dmu89fRQ2kSeujggv9Lix5i0cN/BaIo8vLlS4KCgpKtM2DAAIKC3hMVpQEkqNXRhISE0bNnTxYvXkzWrFkZNGjwBzk0AQRUKjW7du0y68/BwSEZP2Yx3SJKfwq5XM7x48epU6c2SqUMa2sFjo72rFq1iurVq+Ps7EzTpk2xslIQbxwvYmNjzciRI7/KGC38O7CY4CaBRCJh/LSxPO72mKtXruKRwYOKFSuiUChS3Mfjx49Zv3IzgW/eUaVuRZo0a5TkhEKhUODkZo0mOBwrefxk4Z3qPmXrF2P76t08CffFzsYBVzcXvDN6EWB9m4p1itC0ZcO4cZWqWJynq++Szz5+0yFCF4qDh81Hk7pnzulN0O3XuFnFJ51/rXpEoQrmvgTXrl1j+9qtaFQaajepQ606tT/qS6VSqTh27DjhYeFUqFieLFmy8OrVK7as2cgL3+cULVecFm1aYW9vn2wfSREVFcXOrdu5euYi3ll8aN2pPZkzZ/5kO61Wy/qlyxlbqB5yqYyIiHAKK9x48fw1C+bNY9CQIakaB8CDBw/Yvm49ocHBlK9Zg/oNGyb5HrG3t0cjlxCu0eBgZRVXfvWNH0WrfxWfp6CP7UaJoljzswYRzyvglSiKF2P+3kb8hOsVsEM0fcNeEgTBCLgBsVu1bbCYm/2jkUgkjJ06jseP06aHm/7cxDv/d1SsXZHGzRonq4d2blZEBYVhK4vPK/lCfY+S5YuyffUuMz30yujFK+vblK1TlKYtG5vpoe/qu+RJpR5myZmR93de45JAD/1VjyhcPrEeblu9DY1aQ+2mtamdAj08HqOH5T/Qw5dPnlOkbNr08NqZi3ilUg/XLVrB8FzNkEulREREUABPXvi+Zv68eQz+TD3cumYDoUHvqVC7Gg0afUQPFRIiotXYK+MXzVffvqBonTKpvi5Y9NDC1+XEiRN06tSJd+/eYTQaKVeuHBs3bsTT09Os3sGDBxPl9RRFU/sLFy7EnGTGPWNWTxAEvLy8zPqztramfft2bNiwCY0m1gzXtLgbMWJEut9ncjg5ObF//34CAwN5//49OXLkMMvnu2KFyVpk586dyOVypFIp06ZNo169el9tjBb++Xz1E1BBEDIJgnA8JjLcXUEQBsSUuwiCcFgQhMcxP50TtBkhCIJvTOS4Ol9rrLly5aJN2zZUr149VZOtY0eP07vZEN7/5YDztZLsHn+ZH1t1Jzo6Osn6o8YN5Kl+C0FRT4jWR/Iq/ApR9pc5uf8MGXR5uaLawZvAl9x/dJdb749jyOHHtFlTzMbVuWcnnrld5F7EJVT6CF5GPea4cT1Dxg/86FiHjR3ITfkOXkbeQ62P5FnkTR7bH+anIb3j6qz6409m9pxItjvOFHuRld3jNjH8p6HJRi68d+8eTaq2YvvQk5wc95DeDQcx9Ofh9GvZDeWJ91QLycGbDTfo0rxDTK6plBEREUHXVh3xW3+OmhGZcb4QwsA23bl65eon2/r5+ZFJ6YhCKsP/9WvC/N9hb5BQ1tGHXUtWsWzBohSPA+Dg/v1M7t2Pwv7vaSKz59G6LfTv3BWdTpeoriAI9Bk2hGlXT3Mv4A2hahWHfO9zIiqIVu3bpeq6sYipeHxpRFF8C/gJgpAnpqgGJvMzgL+A6gCCIOQGFEBQzN8SoCWw6SsM8x/J96CHx48eZ2CrQciPWJP/cRHOzjhP19bdktXDYeN/5qxhHa9Vj1HpI3gQcZEXjmc5sf80mfR5Oa/azpt3Jj289v446hwvE+lhl56deOF2kfsxeugX9ZjTxvUM/YQeDh87kPuynbyKvIdGH8mLyBu8sDvCgIR6+PufTOs2Be+rHuR7mIsdo7cxrP+wj+ph82qt2DfyOJcn36N/40EMGzicfq26YXMqmBqh2Xm76fP0sFurjvhtOEvNyEw4X0ydHmZUOCOXynjz6g2hb4Kw1ckoaZWVnQvXsmzB4hSPA+DgvgOM7/YzuR5FU1uVgdu/76Zvp27J6mHf4YOZees499+9JlQTxZFndzkVHUir9m2T6P3TWPTQwtfi2bNnNGjQgJcvX6FWa4mO1nPmzDlq1KiRSAMSLsoSIggCOt2HJqoJ36Ei1tZK+vXrl6jtokWLaNasCVZWcuztbbCxUTJy5Ajat2+fLveXkBcvXtCzZ0/y5s1L7dq1OXr0qNnzHh4e5M2bN9F92tjYsHHjRt6+fcu1a9cIDAyk51cM+mjh38G3MMHVA4NEUcyHyQylryAI+THtEB4VRTEXcDTmb2KeawMUwOQ3sVgQkogQ9A/BaDQyfcxcqso6kdk2L87KDBSzrYnw0IM9u/cm2aZ8+XKs2jabHDXeE53pCHW6ZqBkycLkjqhNDa/21MjekmDHOzyQHeSdxzXW71yZyE/HxcWFdbv/JHdXR25l3ou0zlsWbptB2bIf31HOkycPq3YtxrNpFE+z7CVHW4F1u/+IywEXFhbGjuVb6JK5JTkcs5DRzpMWPv8j6Oprbt68mag/URQZ3HMExaL+R1GbKhR2Ks//lN04svIcVawKUNwjLxlsXSnplo/8ak9W//Fxn9ro6Ghev36NVqtl09oNlNB5USdzSbzsXCnumZteWeswe2ziFCqRkZEJnOHB1dWVQE0EGo0GXZQadxs7lDIZgZpIamcvwPGtuz5qSpMQvV7P8llzGFWmMkW8M5HJyZm2BYvhE6Hh8KFDSbapUq0ao5bM57yTgsX+voiVSrF04/qPmsglh4gp6EZKH2lBEISmgiC8AsoB+wRB+Dum3FsQhP0JqvYH1guCcAsoCsTmcVgJZBcE4Q6miVUnMf5bujKmk4KnaRzmv5n/vB7OHjuHNk7tye2YBw9rD6q4Vsf5pSt7k9HDcuXLsWT7DKxr+eObZS9FuztTtGRBikfVpn7G9tTL1ZJAxzvckh3gjcdV1iWjh+t3/0n+ro7cy7wXZZ23LEqhHq7ZtZgsTVS8ybyP/G0kbPhAD7f9sY32GdqQzSEr3rZeNM7QgIBL/snq4dBeI6igrU0Zh8qUcC1PS/vOnFx9lmrW+eP0sJRbXgpqUq+HxXWe1I3RwxKeueidrXbK9TA6DI1GjTZKjavSAYVUTpAunGoZi3J4055U6eGyGXMZmK8uBT2ykNHBleY5y5AhyMjhv5PXw9HL5nHZQ+CP9/cRqhfj903rLHpo0cN/PIsXL0ar1ROfgkRArzfw8uVLLly4YFa3Y8eOKJVyEi4sFQo5uXPnxmCIfTfGLkKNgAErKzk2NkrmzJlDmTKJ9crKyor169fz6tUrzp49w7t37xg1alSqc4J+imfPnlG4cGGWL1/Bw4ePOXz4CI0aNWLVqlUp7sPJyYlcuXKlasPSwvfDVzfBFUXRH/CP+T1CEIT7QEZMocmrxlRbDZwAhsWUb4qJGvdMEARfoDRw/uuOPGUEBAQgV9sn8qvMLC/IqUPnadGyeZLtcufOzax5k+L+rluhCZVtqwPgZZMdryzZAThiWJBs2gMXFxd+HtLf5FmSCjJnzsy4KaOTfO7u3bvklGdG+oG/Tn5ZDi6dvUDRokXjykRRZMKYiTy94YtOKnLU+J6CruUp5VaTPPrSPAp6TT637Ky8sRNNtAqZIOXx3GPUql8nUfhwURT5feFiDm7dSQalHQHRkUREaxiZs4VZPVdrB3Qvo1Cr1VhbW6PVapn621junr+Ii5U17/Raeg8bQo1atchdphg7Llykhn0WAN6owjgU8IBfa7dE8HvArVu3qF69+idfr5cvX+JjZYO13FxUy3n7cO7UaerVr59ku/z58zNh1oxP9p8SvlboMFEUdwI7kyh/A9RL8PcNIJFZW0wEyA7J9H0C06Lru+V70EPbaFtsrMwXiPms83P28Dmaf0QPp82bGPd3/QpNqG9n+mz62GTHJ5tJD3d+IT0c/xE9zCbJgvSDNX8eIRcXz1xMpIcTx0zg6Q1fRLmRvYb3FPMoR8UMNShkLMnjd/7kd8vOius7UUerkAsSHqVSD0flMn/9XK0d0L0w18Mpv47jztlLOCttCTJq6DN8EDVr1yJvuaLsOXWOSgpT1Nm3mhCOvb/D0AIdkQTcTpUeesnsE+lhSdesXDp5lnoNLHqYoNyih/9yfH19Y072E86JBARBip+fH+XKxaV8Zdq0aVy/fp1bt26bagkmS5KJEyfSqlWrGN9QIeYhQaGQsXr1KurXr28W/TYpXF1dk83jmR507NgxJtptfGAklUrDwIG/0L59+2RPdy1YSCnf1AdUEISsQDHgIpAhZjKGKIr+sfmxME3GEm4rvYopS6q/HkAPIEV+MF8CBwcH1GI4oiia7UiF64LJ5JMhxf04uTiiehmOndwprizaoMbKMWWBKdILV1dXQoxJJIs3hpHDs4RZ2aZ1m3j8ly8/2nfHSeKOQTSw9/1O7skdMWAK/LH4yibquxYhv0Mmog06/IzBjOw5kLX7t5n5P23btIXHu44xuXh9JIIEvdHAhCNbOfT0Eq3yx0+KjKIRjaiP8yebOXESro/9mFauFoIgoNJqmTRpGt4+PoyeNJ5+3Xoy7PBfeNjYY6O0on+lerjY2PFOp8LNzS1Fr4mzszNBGlWi8rcR4bjlLZyiPtLKfzN29ffNf1UPI42RifTwvTYYz1TooaOLI5Gvw3FIoIcagxoru6+vh6FiYj0MEUPJ72X+2d+0fhNP9vjSw7kzLlKTHu4M3sVNhYNJDwWRRZc308CtIPkdTXr4Qh/MqF4DWbMvhXr45BKtCnxEDydMxv7uW8YVNUUwVumimTlhJhkz+TBm8jj6devFrwc24WHliLXSih6lmuBsZU+wITJVehiijUpUHhgVhrt36lOqfA4WPbTwtahevTqHDh2Oy8NpQkSni6ZkSfM9Bzs7O86ePcvFixe5e/cuefLkoUKFCgC0bt2azZs3o1JpALCxsWLMmDG0apV0HvQvjdFoZOXKlSxcuJCuXbty9uxZQErCBSgY0Gq1PH/+nFy5cn2TcVr47/DNouAKgmAHbAd+FsUkvtETVE2iLOk4YKL4uyiKJUVRLOnu7p4ew0w1tra2lKpWhHuqc3H+AIHqVxwJXsOhvcdo37QLx48d/2Q/Xfp34Kp2N3qjyYfGKBq4ptlLxx5fV5xy586N0UvKvZBHcWVvo95xmyfUrlvbrO7WP7dR170+UoWA3qhFKkipYVOHG0EneeF0kyAxGAdBQX6HTBhFI6G6SHL5ZKW0VVb+PvC3WV/b16yjbZ7ScZESZRIpvUvV4uDzC6h0JsE2ikb2vLhA+dpVmT9zDi1qNmDjH2tQGOPfMjYKBa2z5WPb2nUoFAoW/LGUjPly0qfi/5hQtw05XDNwJ8CPYBsJhQoVStFr4uzsjE+B/Bx58ijuf/wg8C0LLp/g6L6d9OvcgbNfMN/V1zQ5s/B1+C/rYdHKRbgUeiHus/Im6jWbX23g6L5j/Ni8c4r08Mf+HTirMdfDc6q9tPsGeij4CNwPjU/hEKgO5L78USI93PbnVup7/A+JQoJO1CEVpNSxq8Wlt6d5ZHebAGMw9oKc/I6ZMIoiobpIcmfKRhmrLPx94KBZX8np4YHnF830cPfzi4n10BD/NW8jV9LMuzBb16yP0cMlZCqQg66lGjGq8o9kc/LmQfBzQh2MqdLDTIXzctLvbtz/+HHwG36/8zdHdu+izw8/WPTQwn+GH3/8ETc3V+RyKbHvPhsbK1q1aoWDgwOPHj1Cr9fH1RcEgbJly9K1a1cqVqyIIAgIgsDy5cvZu3cvvXv3JEMGD86cOcPw4cOTve6Xpnv37gwY8DM3b96OiYQdezIbi+l3rTY6xZtTFix8jG9yAioIghzTZGu9KIo7YooDBEHwitnt9wJiEzS/AjIlaO4DvPl6o009Y6eOZoJ0MgePLkSqV/DA7w5VnTuQz1iKyCchTOv3BwGj3tGmffKTp9q1a/FuRBArFyzB2uCISgilVZ/GtGrb8iveiUk85/wxn98Gj+bovYvIBTkyNyWz/pybyEQkWh2Nla0VmbL48PKZHxK9DBB4q3vGbzN/4/rVy7zdf4NAbSgGjLh5uWNjY4uLzI7gAPN83DpNNLZy89MNDydn3Lw9mO23HwejklBdFGVqV+LWlWuU0XsyxKcOj9RZuPDgIW/CwvixTHlTOzt73geaggwqlUpmrVjK+CEj0L24jl404potM7P/WJIqH4pfp05h6m9jGXr2CDIRHrx4wNgGpamcPzv+YZHMnzqa0B4Dqd8opZH7U4do2fP/z/Bf18MxU8YwacxkVhxbhtyo4N6zu7T0bkNx+xKEvgthyS/LeDfkHa0+oYdBw4NYtXAJtjpHIiWhNO/9bfRw7vJ5/DpoDGfunEUmyFB4KJmzbE4iPdSqtVg7WuGTxQe/Z35IDTIQ4bXuOb9O/41rVy8TcOA6gdGhGAQjbp4e2Nja4BxqS3CgeT6/j+nhrJcHcDAqCNWrKFOrErcuX6O0LiODvP/Hw8jsXHjykLcRIbQvVgUAd1sHzgSa0p0olUrm/LmYcYNGEv3oInrRgHsOH+bOWpwqPRw7fRJTxoxj3KVdyIwCD18+YFilKlTIlYu3ERH8MXYcIf370aCxRQ8t/Luxt7fnypUrTJw4kZ07d2JnZ0fXrl05ceIEPj4+yOVy5HI58+fPp0OHJK2tAZOWVK1alapVq3LixAmKFSv2Fe/CnGfPnrFhw4aYqLwfO5cSKFiwIM7Ozh+pY8FCyvjqC9CYnFcrgPuiKM5O8NRuoBMwNebnrgTlGwRBmA14A7mAS5+6TmhoKPOnzyJf0UJUq179o6H30xulUsnEmeNQqVQsmreEjKuLkNfO5EzuoHClkqwdy+ctpWWb5mZ56j6k/Q9tad2uJSEhITg6On4xR26NRsPfBw/he/8JBYrmo0bNGmb2/S4uLixYuZiIiAh0Oh0uLi5J9pO7UG5e3HpOVvts5MydgyhVFPdD79GyVQt+6NKBpi0b0/5qU7yy+WClUBC7o3Y96gV9Kv9g1leOAvm4/+41+T184spu+D+ncu2ajJ40nuDgYOzt7dm2dStv9lwhv48nosGIncKG9pnKM+XxbpoVKY6DlRVnXz2nTMsGcf1ky5aNEZPHs3fnThQKJY1bNCe1J0Q2NjaMnzEdlUrFglkzaBnkQtX8Jt/SjM72jKyenx/GjeaZ7xOq1q5NwYIFU9X/p7Ds5P83+Kp6OGMW+Yp8Gz0cP92kh0vmLaHAtkKUdCkFgIvSleaurVi1cAXNP6GH7X5oS6uvqIeP7j+hUDJ6uPDPRZ/Uw1wFc/Hs3nOyO2QjR+7sqFQq7obcp0Wz5nTs0oEmLRvT4VoTPLP5YKVQxh02XI98Se9KP5r1lRo9fL37Gnm9PTHqTXrY1qcC03130Sh/aeyV1lx4+4Ry7eNPa7Nly8bIqePYu+Mv5EolTVo0+yw9nDBrGiqVivkzZtLI15tKOUzmt94OjgwsXprev/7GM9+nVKtTy6KHFv7VuLu7M2/ePObNmwdA3bp1OXHiJNHReqKjDYCanj17kiVLFipVqpSu13706BE3b94ke/bsFC9ePF2CD12+fBm5XIFGo09QGhuVN94EVyIRWLNmTZqvZ8ECfBsT3ApAR6C6IAg3Yh71ME20agmC8BioFfM3oijeBbZgCmN+EOgrimLymcRjUL8LxeXCG07OWkOXVu2Jikrso/KlsbGxwffec7wUOczKZRIFVnoHQkJCPtmHTCbD3d39i0223r17R4u67dg+8jR+a2Dt4IO0aRjrfG6Ovb19spMtgIGjf+aY5DBX31/mrfotdzS3ueN2g4Ejf45r37ZPZxY/OcC9oBc8DXnDat8juBTPRuHC5v5T/YcNZp3/LY49v4dfWBCHn91me/Ajegzoh0Qiwd3dnTMnTzNn1HRyGl0RQ/X4P3+NTKHkrSYKT7kTl/2eseXeDW7LDDRu2jSu7+WLlzC1d1+87zzE8coNhnXsxJ6//vqs18/Gxga/J48omjl+whYRHkHwKz8co1U4Xr3Bwl8GM2tK4siUn4vF5Ow/xVfRQ01QCJ7XnnNm3kq6tmr3zfTw2YNnZLPJZlYul8ixN9r/Y/SwWd12rBl5kntrYNngA7T6TD38efRADolHuRR0hQB1ADfVt7nufIufRwyMa9+md2cW+f7N3aAXPAl5w6pHx3Apnv3z9PDUaeaOnk4OgxuE6Xj74g1ypYLA6EgyyJ24+uYJOx5f5oGNhsbN4vXwj0VLGN/lZ+zPvkZ65DGD2nRlz1+7+BxsbGx46etLoQT5CyMiwgny88MuKhqbc3eZ/9MwZk226KGFtCMIwgxBEB4IgnBLEISdgiA4fe0xvHr1ipMnTxIdrSN+sSagUmmYPn16ul1Hp9PRvHlzihYtSteu3alSpQplypRJkW5+iowZM8aYzye0JBAAA2BEEExpYUaOHJli03wLFj7FV1+AiqJ4RhRFQRTFwqIoFo157BdFMVgUxRqiKOaK+fk+QZtJoijmEEUxjyiKB1JyHSelDcW8stM2ZzlKaJ1Z/cfKL3dTHyF3gRy8i35lVmYUDaiEsLiQ81FRUdy+fZvAwEBEUeTp06fcv38/LmT+l2TmxLnkCKhECdtaZLMrQGmberg9L8DSBX+kuq/MmTOzZu9qMv3ozdNCD8ndIztr96wlQ4b4YCNtf2jPsGVTeF5AxpWMYRTpVJseA/ok2sXLlCkTf2zbiLRmUY7aRmBbryzLt8Uneo6Ojmbe2FnU86xKoDYKa5k1bgpn9Cot7l5ePBUjueWkIHv7ZixdtwZra1PC85cvX3Jq23bGVqhMxew5qJYzFxMqVGH13HlERkZ+1muYNWceHr01fQmIRpFAf3/crKwxGiWUzZiJUWUr8Pj4Se7du/eJnlKBkIqHhX8sX0sPna1sKO6djY55S1NGtGf18hVf7qY+Qo58OXitfm1WZhANhIvh/wg9nDFxLhkCKlLAtjaZ7PJTyKYe1s/zs+Qz9XD1njV4dfThQT5fsnfJxdrdH+phB4Yuncqz/HKueEVS+Ic6dP/p8/Wwvldl3ukisJFZ4a5wQhelxcPbi+dCGA8yQJ7ODVm2frWZHh7buJshBetT1icvlTIXYFiBBqycsfCz9TBbrlz4BplMiEXRSOCbt7gqbRERKO2VhSHFq/Hw6BmLHlpIDw4DBUVRLAw8AkZ87QH4+/ujUChJ6s314sWLVPf34sULduzYweXLl83yik6bNo0DBw6iVmuJiIgiKkrDzZu36N69e1qGD0D58uXx9vZCKpWQcBGqVCpp2rQJ3bt35ciRw0yYMCHN17JgIZZvGgX3a1HFJx9T/z5Cn5/7f/Vrt/+xDe23dsdR44a7VSa0Rg3X1Pto2acRCoWCFUtXsG35drylPvipXxAU8ZZCnnlRSBQEyt4zZuY4SpZKFM093bh+/hb1bM2THeexK8nRg78zdNSgVPfn4uJCr/69PlqnUKFCvA8KYt74yeh9n3Nhww50znZMWzQfDw+PuHouLi706Ns7yT5u375NNllGKniWZOalRWS2cSOfvQ9yUc5O33NUbFybcTMmJ2p39vRpqnh4xQXzAFDKZJR0cePKlStUrVo11ffculNnhnRtTwZHWzxtFej1RmafvEcJF0/ev3lDtNFIOSdnTh45kii9wudi2cm38LlUzZKHCX8foc+An776tdv+2JYuO7viGuVKRlsfog3RHA45SJMujVEoFKxcuoIdK7fhI/PmRZQfAeEBFPTMi1KiIFAawphZY7+oHl45f5MKH+hhdruSHD/4B8O+tB5OmIT+yTMubtyOztmeqQtTp4fZZd5UdC/BtFdLyWLtRl57HxTI+cv3LBWb1GZ8Enp45tRpytpnMdNDhVROYVvvz9bDNp1/5JcffsDDzh4PK2sMegOL7l6mmJMX71/7E200UsrWjROHLXpoIW2Iopgw0ewFoEVydb8U+fLlQ6/XYW6uCnK5PEVpjGIxGo10796dDRs2oFAoMRgM5MiRncOHD+Ph4cGSJUtRq6NJeMqq1erZs2cPGo0GKyurz74HQRA4duwYrVq14tq1a0gkEry9vVi9ejU1a9b87H4tWPgY38UCVKWLxipmx/dr4+npydJNs5n222yuP/wLpZ2cdj+3oH3Htpw8cZLDS4/Sxa0HAgILns2krcP/8BE88fTyIiw6grH9R7Ph0JbPStCdEqQyCYZoPTIh3sdJZ9SgtP5y6Q3evHnDot8m8VvxatgpTKJ5P9CfYX1/4s+tm1LUh42NDWqjBiuZkj7Fu/DXo/1seX2JSH0UBWuVYvqU8Um3s7UlUK9LVB5lMCRKZp9SMmXKxG/zlrF4xhRe+t7mpe9TOucrSsPsuREEAYNo5PTDFzim4wmOJeSGhc9FpdV+Uz2ct24us8bN5sDjvSisFbQa2JK2MXp4/I/D9PHujIDAjKfzaGtXD2+88MzgGaOHY9hwaPMX00OZTIoxWo/0Az20+sJ6uHjsRMaXqIpdTPqUewH+DO/3Eyu3pF4P+xXvxM5HB9nif4FIvYpCtUoxIxk9tLWz5blRm6hcJerSpIfjFy9m4dRpvLzyiJePn9IxV0n+lyUfgiBgNBo59/QZrsb0UzGLHloAugCbk3syYVoqd3d3Tpw4kW4XXrFiBf7+/nFWGoIgIJVKyZ8/f7LXia0rkUiIjIxk+/btFCxYkIkTJ2JaZJpSV+3atYtcuXIxZMhgDAYD5ietpjrnzp1DIkm7QeOkSZPQ6XSo1WrWr18PkK6v0+cSGRn5jxhHLJbxpA//+QWoKIpse3aZpv3bf9Hr+Pv7M3fyXG5fvotcKaNZx6Z07NIRiURCrly5WL5hSaI2G//YSFW7GkgFKc8inuItcyOHTTaCwgLx9PbEUWlPMWkeDh08RItWX2Zjr2HrupxfeoKSdrUA0+t1VX2E1p2bfZHrAezftZs6HtniFp8A+Ty8UFx/yIsXL8iSJcsn+8iXLx+hdmpeRfjjY+9Fl8LtiNKpWPpmM9PmzUo2yEq16tXpPHce1dU5cLY2TbBeh4XyUKNibIkSSbZJCqPRyMa1a9m/dTNajYZchQozYuJUIiIi6FCrNgVcPeLM6NQ6PcdeviT3ndt0bNIArSoKn+w56T14GDlzfl6ePEvURwufgyiKbH58jSZ9fvh05TTg7+/P/ClzuHv1NnKlnEbtm9Gx8w9xerh0XWI93LR8I7WcqyIVpDwJe4an1I2cNlkJCAvC08ukh0Uleb+oHjZqXZdjS09QMIEe3lUfoeMX1cNd1M2QJW7xCZA/gxeKV49TpYchthr8It6Syd6TbkXaEKVTsej1NqZ+Qg9XzlxIZU1+HK1MUXz9I97zVAylxGfo4d5NW9FqoslduCCjpkwmIiKCdjXqks/FM14PDXpO+D8l393bdGzUiGhVFD45ctJnyBCLHlpIhCAIRwDPJJ4aJYrirpg6owA9sD65fkRR/B34HSBPnjzi55zuf4wdO3Ywffp0AgICqFWrFmPGjCFTpkyJ6r169YpOnTpx+vRpAEqUKMHYsWMZOnQo9+8/xNwzTkSplPH69Wt+//13tmzZhsGQ8L1upFChAty6dStd7+XEiROfZf3wpbCM5+P808aTUv6zC9C36jDWPznP48hAKjSoRaMmXyb8O0BERAQ9WvekgroqXRwqojVGc2LRMd6+fsuw34Yl2y4yPBJbuelLX2PQYCuJXZAJccYcNoI1EUkEwEgvevTtzsvnYzlwfBnORm+CJX5UaVWWlq2/nCVLRGgYPsrE5iL2cmWK/Y4EQWDWH3MZ3GMg9m+UWAkKXooBDJ0x+qM5qhwcHBgxYzpjR4wkt40dWqOB10YDkxct/GgEzg9ZNGcWIWdPMblMEazlMm69fsvPnTvRb9QYKuTPz9L7t/FQWGErl/Mg9D3uTo4YHt1gUoPKOFgpefg2iFG9uzFn9Qa8vb1TfF2ID7phwUJKeRMVzuoHl3gYEUz5+rW/uB72adedutLS/C9je6INWg6sPEXA67cM/TX5PHeR4RHYyU2bQmq9BluJ6ZRWSDDfshWsvqge9urbHb/nYzl7/HfsjV6ES/yo3qrcF9bDcDInqYeKVOvhkJ4DsX+tiNHDQIZO/7QejpkzhUlDRpNN7ozWaCBApmHa0vmp0sOFs+YQeOw8o/NWwEom507Aa376oQs//TqS8nkKsOLJNdzl1tjK5DwMD8Ld0RHtnTv8Wqsa9lZWPAp8x4gePZi3bp1FDy2YIYriR21ABUHoBDQAaogJnSa/Ms2aNaNZs49vVOl0OipUqMDr1/4YDKZ37cWLl3nw4AFhYWEk5UcqkUiIiopi2rRpHDlylMjIKNRqNQqFAoVCxooV38af34KFtPKfXYD6ZM9Ki8m/kC1bti+es2j3zj3kjsxHLpfcACilVtR2+R+r9vxO74G9kzUXq96wOjeWXqOCayWy2Wfn4LNdVNVHIbeSIyAgiiI3tA+YXC3tTubJIZPJmDpnIoGBgbx69YqsWbN+NLJjelCpVg1WHxtP8YxZ48oitRqeqsPInTt3ivvJnDkzmw9s49GjR2g0GvLnz2+WLiE5SpYqxZa/D3L37l1kMhl58+ZNlflKVFQUZ/bvZ0GtinG7+kV8vKgTHs6Thw/w00YztWFj3kZEoNbp6OHmSru1K9jSsikOVqZTjjyebnTIq2Lz2tUMHJbauAmiZcffQqrwyZ6VphOGfhU93PvXbooacpDfwxT920qmpEnG6sw+sIHwn/skq4fVGtTgysobVPGoQE7HbOzW7KOKXoXcyhTxVhRFrmsfMKVaty82dplMxrRvoIdrjo+jhE/WuLLI6GieqMNTrYeb9n+eHm47uj9Nenh67wGmlq4bp4eFPH2oFhWK74OHvDaoGV+3OQGR4ah1Wrq6uNFpy1LWN2yDfYzfWm4Pd9pkz8LmNasZONyihxZShiAIdYFhQBVRFFXfejyfYv/+/YSEhMacYpo+K6JosiDIlSsX794Fo9MlDGou4u7uTqZMmRAEgYcPH/DHH39w/vx58ubNS58+fZI8ZU0JDx8+ZOfOnQiCQLNmzciVK1fab9CChVTwn12AyuVyihcv/lWu5XvnMd5yH7MyQRBwl3jw9u3bZCdcbTu2pfv+HhzyO0B2aU48HDMy6+1KWuapR3iQmkvqO5RtVpkcOXIk2T498fDwMAt48SUpUaIE+0oUZO7Vk1T2yEJ4tIa/A58xYNyoFE2YEiIIAnny5En1GKRSaaJUByklICCALA52iSJV5nVz4eiTJ/QaNowxU6bSIHNm7OVKZlw4j6ebE472dub1PV058vDBZ43BMt2ykBq+ph4+vvuYLNbmFnOCIOAld/2kHvY82J19rw+RU5kdLxdvZr1dQYtc9QkPUnFZc5tyzSv9R/WwELOvnqZKhkyER2s4EPCCAWNH/2v00MfWIZEe5nJy57zvE3oPH8qEydOp65kdO4WSOddO4enqjKOtuR7mcXfj5MOHnzUGix5+tywElMDhmPffBVEUPx716xvy9OlToqMT+1wbjUYKFCiAr68v79+HolarkcvlyOUK/vzzz7jPlrOzM0OHDk3zOKZNm8a4cePQ600+pWPHjmPixAkMGpT6QGsWLHwu/9kF6NckX7H8nD9yiazE57czikYCjQEfNSeytrZm1bY/OXb0GNfOXaNZ9iYUKfEbp46cRKNWM7zBb/+KnEtarZbr168jiiLFixf/ZI4+QRD4dcokrl69yqnDR7B3dGRuk3GpNr0CMBgM3LhxA41GQ7FixT4ZOMNoNHL79m3CwsIoWrToR4OZ+Pv78+jRIzJlykT27Nnjyr28vHgeFoHRaErMHMudgGBy1i1PrTp1yFegAPv++ot3kVH06NOTaSOHEq3TYyWP/8jdfv2OXAVTH9FTBAyCZcpl4Z9JvqL5uX3uFDmcMseVGUUjb7RBn9TDlVtWcezoMa6fv0rD7E0ZXbzo96GHkz/Uw4n/Kj18GRWGUTSaRdO9//4tuarWplbdOuQrWIB9O3fxKjKS3gM7M3X4MDR6HVYJFth33waSq2DqF8EWPfx+EUXx85yGvxHFihVDoZCh1SbMGSoikUioXLkykydPZsWKFRw/fpycOXPSt2/fz/aLTo5Hjx4xbtw41Gpt3Bh0Oj2jR4+madOmZp9tCxa+JJYFaDrQoHF9Ni7fiGOIEwWcCqHWqzgWdpg67WtjZ2f30bYymYzadWpTu07tuLKCBQt+6SGnG1evXGXcwNHklHgiApMN/oyaOY4yZct8tJ0gCJQsWZKSJT8/pcLjx48Z2fcncsrtsJJImRX+jp7Dh1Dnf3WTrP/mzRuG9OqND+CsULAgKIgW3bvRur15gCqj0ciU38bx4PRF8tm78UIdhjKLF9MWzsPa2hpra2tqt2jF3IN76FysII5WVpx7/oLj78NZ3sSU4N3Hx4ee/eLTObTr3pvJqxbTr2wB3O1tuPbSn81PAlkw9vOCwVimWxb+qdRv1IAtKzfgEniXYu75iNKp2fv2FLVa/O+70MPxA0eTU+oJoshkw9uvqocj+gwgu9QeK0HGzKgAeo0Y/FE9HNS9Lx4aBY5SK+ZEvaFN78607tDOrJ7RaGTSmPHcPXGFXFZevNK+xzanOzMWz4nTw7qtW7Dkr79pn6cEDlZWXPR7yllVECubNgFi9LB/37g+2/XsxYw/fqdX8SK42dpy4/Vrtr96zcJpMz7r3i16aOHfQJUqVcifPz83b96OOwlVKGTI5XKaNm2KQqHgl19+4Zdffkm3a6pUKrZv386zZ88oUaIEt27dQq83Yu5vKiCKsHPnTsspqIWvhmUBmg7Y2NiwfMsfLJ27jDUnlmNjZ0Prfq1p2qLJtx7aF0WtVjP2p5H09mqMo9IegHBtFBMGjmHL0b9SFMbfaDQiCEIi862UtBvVbwADspfA28Hk09ZCp2XclBkULV7MLNl7LKN/Hki3TFnIHWNa18ZgYNzKPylSogR58+aNq7dj2zZUl+/xa/FaceM69PQuC2fOZsiYUQB07d2bvRkzMnXtGiIjIiherhwLJs5KdoLdpEULHJycmLnyD8JC3pO3UGFmLp/02WZ+Fp8nC/9UbGxsWLpxBb/PX8qck5uxsbWh5aA2NGne9FsP7YuiVqsZO2Akfb0b4ag06UB4dBQTfxnD5iNfXg9H9v2ZPplK421v8llV67RMmjQzWT0c2X8QrewKkyOTFwCNDQZmL1lHkZLFzfVw63ZCTz/ll5zN4sZ1wu86C2bMZeivJn/Nbr17sdfbm/lr1hEVo4eL+o9PVg+btmiBo5MTC5YvJzTkPXkLF2bWyj8temjhP01svs2xY8eydu1aDAYDLVq0IF++fJ+0lPgcHj9+TIUKFVCrNURFqbC1tcHe3o7kYjWlVncsWEgLlgVoOuHq6sqoCSO/9TC+KufPnye/LHPc4hPAQWFLYXk2Tp8+TZ06dZJt++LFC6b/Oh7/J88xCFCpTg1+Gjo4xSJ8584dMkus4xafANZyBTXds3Bw3z46deliVv/NmzfIwsLIXSDehE8uldI4azb27dhJ3pHxgS/2btpG/xxFzMS4ZrZ8DDtyEGIWoIIg0LBxYxo2Tnk00eo1a1KydGnmTB3PnSvnGNTtPDnyF+OXkWNxd3dPcT+WqI8W/um4uroyYtyobz2Mr8r58+cpIM0Ut/gEcFDaUlieNUV6OGPsON4+fYYBqFC7Fj8NSZ0e+mATt/gEkx5Wd8nBwX376dSls1n9N2/eQHAUOfJ6xZXJpVJquxdg77a/yDs6Plrxrg3b+DFjJTM9rJyxCJMObY5bgAqCQMMmjWmYiujKsXo4e8pE7lw6x8AfL5CzYFEGjf7NoocW/rPY2toyY8YMZsyIP+3/UjkcO3bsSFDQe0RRAAQiI1VotVpEMTbQUbwZsCDwySi+FiykJ2nPXGvhu0Wr1SIXE4fql4tStNrEjvaxhIeHM7BzD+qJHkwu2YgpxRsgu/SEsUNTHv1Qr9ejkCS+tlIqQxetS1Su0+lQypKoL5Oh1ajN62q1KKTmezOmqMRpm+aIosiQft0oLX/Eum4FWdu1IPXc/fmlV6eYBNOp6CsVDwsWLHx5tFotciEJPeTTejioS3eaKp2ZXakucyrWxvrmfcYNSz5lzYckr4dSdEkEPdHpdCgkifeflVIZ0Zpo87paXdJ6aEybuoiiyOA+PSiufsHK5mVZ2bwstZShDOze2aKHFiykkdDQUK5du475YaeAVqvH1tYWKysFSqUcpVKOlZWcmTNnkjVr1m80WgvfI5YFqIXPply5ctzSPiPaED/B0Rn03NA+oUKFCsm22797L5XsfMjhYoqUKREk1M1emBfX7xIYGJiiaxcsWJAHqhAiouMXj0bRyLHAF1SvUytR/cyZMxMEBEZExJWJosjBly+o0aCBWd3qDeqx69ENNlw/y4TDO1h+8RiHntyhQAnzKKK3bt1i3IhBDOzZiU0b1qHRaD465rt37+JmDKJGQa84M7syudwp7KLl7NmzKbrvuLELKX+kBUEQZgiC8EAQhFuCIOwUBMEpiTpWgiBcEgThpiAIdwVBGJfguQkxbW8IgnBIEATvmPLSMWU3Ytr9t+0zLfznKVeuHDejE+vhdc3Tj+vhnr1Uc8lALjeT+alEkNAwd0H8bt5JlR4+0gQn0sMTwc+S1cNQuZ4gVVhcmSiKnAh6QK1G/zOrW7NxXQ68vMj2+8eZeXY9a28e4LjfNQqWKmpW79atW/w2dAgDunZm0/r1KdJDF00I1fJkjtPD0tm8KGBrtOihRQ8tfEEiI6MoXLgQXbr8yNSpk3nw4AF9+/b9dEMLFtIRywLUwmfj6OhI9xF9mfd8KydeX+Hk6yvMfb6ZzoN7fjR3nt/TZ/jYOCUq97ZxJCAgIEXXVigUDBr/G+OuH2PPo5sceXKXsZcPU7l10lHcBEFg5NSpTLx2me1373D00SPGnzuDT/nylCpVyqxurXp12Xb3KmKIgaYuRXCPtmXF5VP8r3mTuDr79uxi0Zje1Hf246eiIhHn1tK/W8ePnnS8efOG7C6JTx2yu0h58/pViu47FiNiih9p5DBQUBTFwsAjIKlj6miguiiKRYCiQF1BEMrGPDdDFMXCoigWBfYCv8aU3wFKxpTXBZYJgmBxCbDwr8XR0ZHuw/sx9+k2Try6yslXV5n9dOsn9fDVs2dktndMVO5jZ58qPRw84Tcm3znEPt8bHHt2h0k3DlClbeNk9XDMzEnMf36Uvc8uc/rlHebc30+2GiUT6WHtenXZ43sO3TuR2sryOEU4sf72Yeq3jDe33bdrF3MH/Uw1bTjdPJ14t28nfX/s9Ek9zOqgTFSe3UFp0UOLHlpII05OTpQsWcIsUn+swbrRaOTSpSusXr2aTJkykSVLlm81TAvfMd+VwD169Ig7d+6QMWNGSpUqlapk29+C8PBwdmz7i9uX75G7YA5atm2e6qToBoOBI0eOcnjPCZxcHWjdoXm6Jhxu2KQRZcqX5ejhoxiNBrrUGoGnp2eSdQMDA9mxeRPXb17ltX80BTx84v4HRtHI08hgsmXLlmTbWDQaDft27+bGhQt4ZsrEmHmz2LFtO6qoSAaNnkaxYsWSbVuoUCFW797N0SNHCAkOZnClSkkmel+/YhUDSjQgv6MXGrWa8spMFMxTgDWLfqdixYrodDrWLpnNvKbZCQ8JQhMSQv0CzqhvvOfQwYM0aNTI7J4vXLiAtbU12bJlY4efmg6iaOZPdckvmg4dUpte4usYk4mieCjBnxeAFknUEYHImD/lMQ8x5rnwBFX/3955h0dVvG34fpNseichCQESQuggIHxUFVB6VRGQLqBIU4oKCPxQrCBYUJoURVBARJQmICjFRlOqFGkhoSUBkkB62fn+2CUkISQbUoG5c+2VPbNzZp4t5zlnzsy845QhPeOi4fYU1RvSpHMv+uEPGfzwmXz44aZ12/Ao5UbPQvbD51pNyN0P/9lH+LV4avn6Z/LDk9ejLPLD9WvXcuCvXfiVK8/kz2awetX3XIiL5dX+U3P1w69/Wm32w2tMePTlbP3w6wVLeKFKdyo5lichIQFfO3+qW1Vn8WcL0/1w8Wczea9RHWKuXiU+7gatyvoQf/ZCrn64KvwGvbL44b7wWPrV1H5YcKo1DypLliyhadOmxMcnEBsbx63Z0taAEB+fyMiRI3n66ad1ACJNkfNANEDT0tJ4bcQ4Qv66hF9iIETySzQAADfISURBVDF2m0ku8wnzvpmd5wuYoiIyMpJ+T72A75Va+NpUYd+2ML5f0p+F382mfPnyuReAKTLi8BdGE7YrmTJWdQlPvcHzq19j7PuD6dCxfYFpLV26ND1798wxz/Hjx5k8fChdg/wYXsGbSQd+4cvfUujeoAWxqUl8d+Yf2vfuluMyDfHx8Qzp05c61ta09i3Db7/+yqCPP+DZxrUIcLJn+tiRPDVwCN169rpjGc7OznR58skctf7z1x7+V7ENVmKFk5MTAC5A5KHfAAgLC8NN3eDSuVO4O9jgaG1F1OXzBBhS+GfXjvQLrmVLv2TjigW0quJIWLJiwekUfAMr8/Gmk/RpUg4ba2HVngsYvarkaamJuwi64SUi+zJsz1dKzc9bEQAMBL7N7gURsQb+BoKB2Uqp3RleexfoB8QALTKkNwS+AAKAvkqp1LvQpMkjaWlpjB/5GhH/nCPY2p9fiWameyKzvppXov3wuadfoOzVWvgbqnBwexirl/Zn/sq8+eHQF0bx3+5ESlk/xKnUWDb98AoT3htCx2Lyw6cDyjA0wIfJh7ayYFsyzzZ+jLjUZJYdO0i7Z3vk7oe9+/IQtjxR2p8/tuxgwIzpdH34YXwcHJk2egxPD36B7r3y54d//7mXl7y6ZfFDFyJCLwMmP3RJTuTi2TO42Rqwt7Li2qWL+Kcm8vcfv6f74TdfLeanr7+gub8nsalGPg+/QZmginy68wi9Hg7C2sqKHw+FoMpU1H6o/VBTAAQHBxMSEsIPP/zAwIEDSUq6uf7ozcamEBERQUxMDO7u7sUnVPNA8kA0QFd9+z1RO5Pp7NYPzJHw/zt/mKmTP+CDWVOLV9wdmPPx5wRdeYSKzqaFub3xx/WGN9Pf+oTPFn5kURm//fYbobsTqe10azqJT1pFZkyZTavWLQsl7Ped+PjtKYxvWJPynu4AfDXoGd7ZsI1xe3+geq1aPDNpJI8/8USOZaxctpzGtnZ0rladNKORj7ZtYsmTjUmzsSKwYhBP1kljzJfzaNj0EYsvSrPDx9eXy7HRmSJKJqQkY21vi1KKtLQ0Dv0XQmDrOumBjTydDPx+6iKnroYBcObMGX5dtZD5A6pjY23q1ehSL57XVp+lxYDhvL92FalpKTzefgBDuvXI493HPA8lu6KUuuMCgyKyFcium2aiUmqNOc9EIBX4JltFprB6dcxzon4QkZpKqSPm1yYCE0XkdWAE8IY5fTdQQ0SqAV+JyEalVM4TxzT55vuVq+BADC8E3vKFg5EnmD5lKu/P/KAYld2ZOR9/TpWrj1DZxeSHpe39cY/15sO3PmFmHvzwvz1xBDvf6pErlRbEtCmzaF0Mfjiufq10P/yyfzfe3fQro3b+RPWatXhq/CsW+WEDawc6Va5FmtHIx9s3Mq9FK9KshcCKFemYlsbEBQto9Ej+/LC0nw8RUVfxdboVmTYxNQlre0O6Hx45dZry1YPS/dDDwZ5dF8I5de4cYPLDrcsWM7N9o3Q/7HA9jv/9cZwWz7/IjDWrSU1N4YmOPRneXfsh2g/vWy5fvoyNjQ1eXl5FUp+DgwO9evXizTencPLkKTKv/wkGgyHX9Zk1msKgZI+5KiDWrlhPPcfHMqVVcq7J/j8P3nE9pOJm1459VHDKfBe4jEMQxw+etLiMHVv/pLSqkSnNYG2PY4ofp0+fLhCdlmA0Gom+dDH9YgvA0dbAxA7NCaxYlllfLcr1Ygtg9/ZtPBoQCMDx8HCqeTvj6+KESk1FGRUGa2s6Bfvwy+ZN+dLbe8hAvj77FwkppvlLKWlprDj9F3UfaUjfJzswYVAvkpJTWXswIj0y7uXryaw7fIWIC6YLrq2bNtC1jlv6xRaAj7sjVbyEylWqMXvxCj5f+j09evbGxiZv94FMd/wLbs6TUqqlUqpmNo+bF1v9gY5Ab5XLAaOUiga2Y5rHlJVlQNds9jkGxAGWd3to7pqfvltHC5/M8/we8qrM4V0HSqwf7tm5j2DnzD+Pso5588NtW//ETVXPlGZjbYdtqk+R+2HUxdv9cELbFgQEluezxV9Y5ofbtvNIWdP8zuORl6ns7o6PkzPGtDSUMmKwtqZN2XJs3bQ5X3r7DRvA6ohtJKaaouOmGlP58dI26j3WgN4dOzO2b38SU1LZeCYUo/n3ExmXwOaQ84SHhgKwZeMGOgf7ZvLD0q5OBDsbqFy1GnOWLGP+su/o0Uv7ofbD+5P9+/dTo0YNAgMD8ff3p2nTpoSEhBRZ/RMnTsDR0YFbo7sVjo72DB06NM/HnEZTEDwQvzpByG5KRQm91gLAxc2ZhLhYnGxc09NSVBJ2DgaLy/D2KcW/aZdvS0/iepEOtxAR0sSK1DRjpguQyBtxeHiWsrgcj1KliIyNxSPDgu43h1/dvGOuMjy/Wxo3acK1sUOZOnMOTmnWXDcm8Vj71uzdupb3Wj9EfLI/M9ZFcPxiAn0OHMHdyUBqGrSv4cOOOPf095zdz0upglnsuajWvRORtsA4oFmWeUoZ83gDKUqpaBFxAFoC08yvVVJK3WwldAaOm9MrAGFKqVQRCQCqACGF+mY0wM3fZjZ+WIKnnbm4OhN/ORZnQwY/NCZh62i5H5b2KUWSMey29BRuFL0fWmXvh+6lLB8C7V6qFFfiY/FwdMqQqlAZ51QWgN80btKYq5OeZ87H87BPMRCnEmjeuSW7Nm1g0v81JSEpic9irnIyKoYhm3fibmdLKtCqQnn22Lumv+fszrcF4deg/VBTsrl69SrNmzfn+vW49LRdu/bw6KOPcubMmSLR0K9fP8LDw3n77bdRyjQVY8CA55g6tWSOAtTc/zwQPaBdendiX/zOTGknYg9R/7G6eTr5JSYmsnDeIrq2fpY+XZ5j3Zp1ee4xUErx8+afGfjMILq17s6cT+YQFxd3W77+Q3uyN2k9RvOCwUZlZF/CRnoMtHyh4Ke7deGSw24SUm8tPXI+7gjlqnvi5+eXw54Fi4jQomMnVhw4mv55JaemsWj/MZ55bqDF5XR77jmWHv+XxJQUqvr4cDQiltNXo3B2dQUxlbn+VDgt27bLvbBc6NC5E99t2cCstcv4ftsm7OyEnjXKUMrZkXKe7sQmQeeavoxvWZEhTQJ4s11ltp6IYuDwMQC0bNuBVfujSUm9dWl08Voc/12DqlWr5ltfETIL0xTYLeYlAuYBiEgZEfnJnMcP2CYih4C9wBal1Hrza1NF5Ij5tdbASHP6I8BBETkA/AAMU0pdKZq39GDToUdnfr28N1Pagcjj1GlSL89+uGjeIp5t150BT/dn/V364ZbNP/N8jwE82+4Z5s7M3g/7Du3Jn4mZ/fDP+I10z4Mfdu3Whev2f5OUwQ/D445SoVqpIvfDxzt2YsWhLH548Cjd8uCH3Qf05+uTh0lMTaGqty8noqM5E3UNZzdXQEhOTWXzhfO0apdd51ve6NilI6t/Xcv8jV/x428bsLUVni5fEU9HJ/w9PIlNg3YVAhhV7yEGPFSVcY3rsuPCZQa9bDrcW7XrwJpTl0jJsL7npZgbnI5L1X5oQvvhfczSpUtJSUnl1vxLwWiEmJjrbNqUvxFbliIijB07litXrnDw4AEiIyOYNWuW7v3UFBsPxC+va7en2fv7Ptb8thi/5ApEGyIxBsQzb8psi8swGo0M7jMMu399ecypF0nGRJa+vo6jB08wbvKrFpczf9Z8diz8gxYurXC0ceLAV38zaMvzLPnhq0xzkNp3bM+lCxGsWDAbF6MX17lCxwGt6d3vzgElsuLj48P0z99g8qvvwQ0nkomncoOyfDBzmsVlFBQvvvQyH71/g5c3b8fX2YHzsQk8O3gIjRs3triMunXr8tRLLzH2s8/wt3cg0cmVV3ae4OmHq+B0IZodYVF0e3EEZcuWLRDNVlZWuLqa7uBHXLxIQ7dbPQ0PBwXy0neHaVfVG183Ozb8e4ZIceaxx0xDvStUqEDbnsN4cfFcWlR25EaS4s9zKbwxfW6BRBstgOUELEIpFXyH9ItAe/PzQ0C24TaVUrcNMTOnLwWWFpBMTR54+pmu/P3nPj7fs5pg6zKEE82NUml89sZci8swGo0M6zcEvzB3epfqRGJCEmvfWcXxw8d4ddJYi8tZMHs+u5Zuo4N3M5wdnNiz6iAv/DKQxauW3uaHly9E8O2C2bgqL2K4Qofn8u6HH897k4mvvktqrCOpKp6q9cvxYTHMe33xpZf56EYsL2/Zhq+TI+fjEuj5Yt79sOvI4Uz4dBZ+do4kuzgz6dB+OtWogeOVK/wZGUGPYUMLyQ8vUdPFJf21OuWDGb9jFy0DyuLj5MjmkDCu2dpn8sP2zw1m5JfzeczfgxspRvZExjLlk1naD9F+eL9z+vRpEhISydrnk5KSSmhoKDVq1Mh+x0LAzs6OihUrFll9Gs2dkJI65ye/1K9fX+3bty9T2unTpzl69Cj+/v7UrZu33s/ffvuNucOW8ZjjrcAdSinWJM/j660LLJpQHhsbS/fmPXjOdQhWcivc/ooLS/Ft7kO7jm1o3boVdna31kZLSkoiPDwcb29vHBwcLNabEaPRyMWLF3F0dCyQKJdGo5Hff/+d44ePEhgcRIvHW2AwWDYULi4ujqtXr+Ln52fxPllJTU3l4sWLeHh4YGtryx9//EFiYiKNGjUqtCiei79YxLYvZ9Gkgh+NKvoz5fvtzGjeiC2nL2Dn7kn9wPJsOxtKmSe707V79/T9rl27xq5du3B0dKRJkyY5BjoRkb9zCo5xEy+nYNW55nSLtX+552mLytXcvxSGHy5/7Uue9r012kApxZzLS1mw4UuL/bBXy26MKts3kx8uOvkdHo3K0bZja1rdU374r9kPH38A/PALNs+ZT8My5ahfPoBpW37i3QbN2X4hBHsPd+qVC2TH+RDKd3+SZ7QfakoYVapUUSdOnCiy+pYvX87gwYOJjU3gVhAghZOTPb/88gsJCQk0b968yPTkxvbt27WeHNB6csZS734gekBvUrFixbu+83Nw3yF8UjOvySYi+BgDOHnypEUXXCEhIfhKmfSLrRRjCguPLiT6hoHr39sT9tuvfPbBAhZ/O5cyZcoAprtV+YlgCKY71wV1Fzw+Pp5h/V7A84oVlex92Za8h0WfzGXO1wspVSr3+ZxOTk7pofzvFhsbm0yfyeOPP56v8nJjw9q1bPxqCXUMHiReTGTcrq1429tyNS6ZlrUfwtXN1CtQ19ebX//5O1MD1NPTk/btC26Jh5vcn7eNNEVJfvzw8N+HCLIulylNRAiw9s+TH5Yz+GTww1Rm7v2a2GsOpJx3YPlv25n9wUIWfTunRPvh8H7P4xUNlR1K81vSX3wxcy6zly66j/1wHesWLqOaKk3shWQm719PaUcDUUmJPF6zNq5ubgA85OXDn3//A9oPNQ84Xbt25a233uLs2RDzUijg4GBH48aNadCgATt27ChmhRpN0fNAzAEtCAKCA4iyCr8tPcrqMv7+/haV4evry9UM0zr+DP+DtDh/Klm1p7xbbao6t6RMVGv+N/adAtOdHVevXuXUqVOkpKTked/F8xdRLdqDXhVa8H9+1ega8AhPSBU+eW9GISgtHoxGI2fOnOHy5ctcv36dxR99xPuPNaNfs+a0rFWbVxo05cTVGDx9y6Q3PgHOXIvBv0JQtmVdunSpQDWqPPxpNAVN+YoBXDbePkUt3HglT34YnnItfXtb6G4MUQHUoyPBrnV52LE11a+25c2x7xaY7uzIrx/WjHWlb/BjNPSvSregJrQxBPPJ+5b3yJV0svrhwg9mMumhjvRs1IoWVeoztFpLTkZfw8vPL73xCXAuJgr/ChWyLUv7oeZe4PTp02zYsCHfUbJtbW3566+/eOmlEZQt60eFCuWZNGki69evL5AgXBrNvcgD1QOaH1q3bsXnMxZx7upJApwqYVRGjsTuolzD0hbfkffy8qL6I9X4fcd2Grs/yoHIw5ShI4lWCZRxNy075u1YgV3//oTRaCyQuTEZiY+PZ+LoiZzbH4KblSsRcoVhE4bRoVMHi8vY/tNWRpfJnL9u6Ups2L2yQLUWF7t37WL6xCn4WDkSl5JEpFUSj3l6Ym8eHufh4YmHhycV/j3C1/sP88IjDTFYW3M68irrL4Qzr1u39LL27tnDjDcmUs7RmhtJKYiHD29/NBNvb+87VW8RKu/r3mk0BUqr1q1Y9PEC/os+Q2X3IIzKyF9X/qZ0Tb88+WGVJjXYuucPWvg0Yt+F41TnKeIlCR93U49nGccgNhxZX2h+OGHUJM78HYKLuHPNOoIRk4bmyQ93bNzK2PKtMqU97FuRNbt/KFCtxcXuXbuZ+vrbeKU5E5+WyDVDPA0dfLGzMfuhpycenp4EhO1h+ZH9DGjYFIO1NWeuRrIp/AKfd8/ih5Mn4W9nIDY5BSnlzTsff6L9UFPiSExMpEePHmzZsgVbW1uSkpJo3bo13377Lfb29ndVpru7O9OnT2f69Pvn5pRGkx90A9RC7OzsWPjtXN6bNI1Vf/+EWEOLbo8y5vW8BbCY8sGbzPpwFot/nEdY2jncbWMJKl8Va2vTAt5KmU6nhXFX7M1xb+J+wJUnSj0HQFJaEgvemE/FShUtjkRosDWQlJaCwfrWTydNpSHW935nemRkJFNfncRrVVvjbm8aFrfq6B+cO38a6mSOKdGwYiVCvEox8te/EKMRn4AKvD9vfvqcqytXrjBjwqtMbVkHDyfTXLWD58N5/eXhLFyez8a6gNI3TTXFiJ2dHXOXfc4Hk6ey4cCvYCU81rYZ08a/lady3pg6hdkfzeLjtUs5m3iecjZxBJerltkPJa1Q/PCNsVOw3+PBs26mBmRyWhJzJy3Mkx/aGGxu88NUo/G+8cN3R01mWNkuuNqZFqpfe2oHoRGnTYuEZKBe+aqEV3Bk7K5tiNGIb2AAUxfMy+SH08eP5Z1H/w8PR5MfHrpwmfEvDWfRCu2HmpLFhAkT+PnnLSQmJpOQkAIofv55CxMmTOCjjz4qbnkazX2BboDegbCwMGZOncXR/cdwcXOh//DetO/Ynk8Xfpyvcm1tbRnz+hjGvD6GjRs38dErKzEYanEu5iAHI7ZxPSmCMsGunD17lqCgoNwLtJC4uDiO7TrK4FLPp6fZWdvxmN0jrFz8LZOnvmFROZ17dmXD/I10r9As/aLwlwv7adGhVS57lnw2rFnLEx7B6Y1PgM5VGjJgzW/M3rGdY5fDUUpRw9+fw0mJfLP4S5ydnbMta+P6dXQKKp3e+ASoXdaH1Sf2c+bMmXx/t2n6jr+mCAkLC2PWB59y/KDJD3u/2Jd2Hdvz0fxP8lWura0to8ePYfT4MWzauIl5Y76jmuEhTl4/wB8RW7mWHIFvIfnhv38epbfr0FtarO1oaNWMb79cyRvTJltUTueez7D2i/X0DH4k3Q+3hh2kRYfWBaa1uFj/4zqaOlRPb3wCtKvQlBG//MrCPZv47+pFlDJSrXQAx61iWDFj3R398Kd162hfzi+98QnwkL8vP54J036oKXEsXLiQxMRkbgUMEhITk1m4cKFugGo0BYRugGZDZGQkg7sNo2FCa7o7tSDu6g2+GLecqCvR9H7O8rD/udG2bRsO7z/KkvnvcO2yNd42j+Ph7IqbUdGn+whW/ji/QINlOFo53taT4GrrStiVCxaX061nD/47eoIPt/9AkL0P55OvUrpmAK+NGlEgOouTmKtReBocM6XZWttgsLYlJS6OV+rWxVqEzSEh2Lo45xg85HrUNQIdbo/w6OVgS0xMTL50KtBzmTRFRmRkJMN7vkh7u0do492H68mxfPvWV0Rdi6ZXHpZByY02bdtwZP9Rls6fQkS44GbTDG9nV1wU9Oo+nFU/LihQP3SwcrrND10Mrpy9cs7icrr17MHJY8eZtmMdwY7ehCZG4VUjkPdG3h9+6GqT2eMM1jYYbEx+ODS4CVYI28NPYuebczCl61HXKG9/ux+WsrfTfqgpccTHx+cpXaPR5J17f5xQARIeHs7nn81jcM/BuFzyIcApGBHB2eBKO9ceLJmzjNTU1AKrT0R47fXRuPu4USeoL8GVqhAUFEgp5wo4JTTm8zmLC6wuLy8vUpxTiEnOfLI/GHuIZu2aW1yOlZUV/3v3TT75fiEd3x7EO998yoy5M3MMp3+v0PSJ5uyODsmUduLKebwdbHi5bTt8ypallH8Znm/ZkiBrG/755587ltW4WQu2h5qCtCiliImOJuRcKNtPhFgcpCUnjOZ5T5Y8NJq7ITw8nPmz5zGkz/P4XfegknsFRAQ3Oxd6le3M8nlLC9wPX3l9NM4+rlQL6kVQpSpUqBiIh3MgtokNC9wP01ySuZ7FD48mHqBF+2YWl2NlZcWkd6bw0Xdf0PaNF3lr6SxmzPn0vvDDR1o2Y3/8qUxpp6JC8TLYMqRFF3zLlcW7XBn6P9qO8mn2Ofphk+Yt2HHBFMTPaFRER0Vz9lwoO0+e1n6oKXE0adKEW7GVFWAEjDRt2rT4RGk09xm6AWpm7569DOw4gPBvLlD1TEWMcTF8e+ZzjCoNABsrA05pzly/fr1A601JSSEt2YC7a6lM6925Owby7+GCW6dKRJgwdSLLr3/LP1f3c/ZGCD9FbiShciLtO+U9LL6vry9NmjTJ95IIJYl69erhXrcS849u42hkGHsu/MfHhzfzaKVKWFlZ4ejoiJOjqdekmosLp06evGNZdevWxbFKbd7/5W827drHzsPHeHvzHmqXcmdE356EhobmS6uO+qgpTPbu2cuLT/Yj7oeTNIjwI+V6DHMPfEWaMgJgsLLBVZwKxQ9TU2xwy+KHro4BHC5gP5z0wQTWJH/Noai/CY09yy/R60mrHq/90Ey9evXwbhjI0pBNnLgWwj8Rx/n89I80qlD1Nj8MtvPM1Q+dazzEtJ272bxnH7//e4z3t/1JLc9SDO/dW/uhpkQxe/ZsnJ0dETECadzsZ3dxcSnQm24azYOMboBi6qF6b/y79PV4lvpeD1PZM5jWLi0oZXTiaPR+AFKNqcRZx+Lq6ppLaXnDYDBg5wipaUmZ0qMTQqleq3KB1lX//+qzYO1CSvX05mrjKLq88xTzln6ep7v1iYmJ7N27l2PHjqFU3k/moaGh7N69m6ioKIv3UUpx4sQJ9uzZU6hDYESEt6a/T7e3R/NfJXtiGpfjjU+nc8F80Z2RE3GxBOWwhqKI8Ob7H5AWWIOfTl3hYootEzu3ZlK7xxj5UBAz37v7pXZu3Y+17KHR5AWlFNMmvM2w8p1pUuYhqnsH8aRPE7yVA/+EHwZMfnhdxRWKH9o73O6HNxLCqFkIfvjF+gWUG+BBQosIuk/rzOdfz9N+aEZEeHvGe/T5YDgXHlaktizFW3OmEWFz+3I1Z5Kjc/fDqdOgUjU2hV7mYpoN49u15/UnHmd41cp88u7dL7Wj/VBT0NSqVYvOnTtjZWUN3Hps27ad9957r5jVaTT3B3oOKHDp0iUc4u1x9XIBoJSXJ2ejzlHdtjJ7og9TwaUq2+PW0WtUd2xsCvYjExGGjxrAjDdXUNa+HXYGF2ISzhNr+zuDh3xeoHUB+Pn5MXz08Lva96f1P/HZW7MpawwgUeJJ9krk40UfpS8SnxOmJQ9eIeZkGGUd3DlxI4LHnmzP8DEjc4xwGR4ezmtDRuAam4qbwZ6psVd4btQwOj/91F29h9ywsrKiadOmmYbarF2xgh+OHqVD5cpYifDL6dNEOjpSv379HMsSES6dOcWMZ9pil+F3U83Pm9C/f8+HSj2UTFN4XLp0CedkW9zMwWc8S3kSEh1CHZeK/BFxlGqewfwYvoVug58tFD8cMWog095cho9DG2wNLtxIuECi4U8GD5lfoHVB/v1w7nufUtGmDPEqkVi3VGbM/8RiP5w0ZgyxZ0IIcHZhZvQ1mnbswLDRoy3yQ5fYVNxsHJgaF1nkfvjjspVsOLOfVgG1sBJhZ9gxojwMFvnhxdOneL9Tp0x+WNXHh8+2/poPldoPNQWL0Whk9erVpKUZyRiIKD4+kTlz5jB5smVByjQazZ3RDVDAycmJeOOtO8k2NgYCKpbnz9O7OJN8jF/dvqPf+F50erJTodTfrdvTuDg7MefTxURcu07lOhX4ZNKnJWo4V2hoKHMmz6OP6yDsrE1D48KiQnll8GssX/9Nrvt/MnU6lcIVj9dqA4BRGfl8w3a21KhG67Zt7rjfhJdH84xTEFUCTRd1yampvPfxXGrUfoiKOdxxL0g+mDWLRXPnMm7jRpRSNH7iCWaOGGHR0hAurm5Exyfi43orOmRKWhpY5+/Q08sOaAoLJycn4lIz+KHBQPkK5dl5Yh9Hkk+TnLKRXq/3pVOXwvNDZ2cnZn/6JRHXrlOldhCvT/qsxPnhwrdnMap8D+xsTD2mZ2MuMHboGL5esyLX/T+dNo1aN+Jp3eRRwOSHM7f8wpYaNWjdJic/HENXxyCqlDfNm0xOTeX9IvbDGXM+ZeGcebyzYTNGo5FHWj3OZy8Pt8gPXV3diElIoLSLS3paSloaYl52527RfqgpSFJSUkhOzhgF9xYFPe1Ao3lQ0Q1QwM3NjYDagRw++i+1PGoAoKwV/7mf5vvl31G5suVDv3bs+I0P3pvFtSs3cPNwZPSrg2nTNveQ/G3btaFtuztfeBQHSilWrVzNl7O/IfTsRWyuCxcrn6eCq+lCp5xTeXaHK0JCQggMDMyxnN2/7uC9ul3S06zEiqcDH2blNyvu2AC9ePEiKvI6VWrf6lGwtbGhvW8V1q1azahxr+Xp/aSlpbFwzuds+n4dxpQ0ylcO4pU3xueoHUxrHg4bNYpho0blqT6Ap/v044vZHzKuWX2srEwns5UH/6NFx7u/eDcNOdN3/DWFg5ubG2VrVuTv0OPUK21aD9NoLRy0P8+Kn7/Psx9Oz+CHoyz0w3bt2tCuBPrh9ytXs2TON1w4ewG7BMU590tU9gwAoIKbP9Zh+yzzw+07+LT5E+lpVmLFs9Vq8uWy5XdsgF68eBFjZAxVajVMT7O1saGdT9W79sP5s+bz03cbSEs2ElgtkLFTxlrkh8NHj2T46JF5qg/g6b59WTxzJq82boiVlWkG0Kp/j/F4J+2HmpKDnZ0dtWrV4uDBw2RshIpAs2aWBynTaDR3RjdAzbz94TuMf2kc+47vx83KlUuEM+yN4Xm62Nq9ew+vDp+Ov11nyhtcSY6O5X+vzMXaxpqWLZ/IvYASxoplK1ny7kbq2vcjQMUQmxLFsmPfM6BWb8o4mu7A22JHYmJijuUopbBSgpVknnLsaLAjISbhjvslJibiaHP7fCxHgx3n4/I+9+mDt94n7rczjAt4EoO1DaejLjC6/1C++HEZHh4eeS7PElq1aUNYyFmGfbeCyp6unIuJpUqDxowf/lK+ytXBNDSFyZQZ7zLh5bH8fmo1HjbOhKVe4cWJL+fZD18bPp1ydk8SaPbDya/Mu2f9cOWylax6fyPtnfoRbR1DbOoVvtzzI0ObdKG8qx8A9mKwzA/hNj90srUlMYc5nYmJiTha3+6HTgY7LtyFH06bMpXwjZcYVKo/BisD586E8lLvESxZv7TQ/XDktysJdnMjLPYGVRs24qUR+Vu2RvuhpqCZN28eLVu2JCkphdTUVGxtbbG3t9XrgGo0BYRugJpxdXVlzldzuXz5MtHR0QQFBeU5lP4nMz7Hx9AWO4MpMIetjTNl7Dvw6YcL77kLLqUUX85eRgOHQRis7HB1MxIfFU9V1Y5t57fTu3JvYlNiuWZ7hcqVK5OUlMS6NWvYs30nnqW9eaZPb4KDgwHTPKJyVYM5ceUCVbxuhdz/NewoLbq1vaOGwMBALqsEohJi8XBwTte1PfwUvV8en6f3c/36dfZt/Z3XKz+TPlSsooc/TeMq8uOq1Qx4YVBeP6JMxMfHs2b19xzc/Sely5Sla68+BAQEICIMGjKUZ/v2IzQ0FB8fHzw9PfNVlwJS9QWXphBxdXVl1uJ5+fTD+fgZ2mXyw7L2He9ZP1wyZxldnAZha23yw8ToOBpbtWXzqV288PBT3EiO47JVVLofrl+zhj07duDp7U3XPn0y+2GlShwNv0x1H9/0On4+/R/N2re7o4bAwEDCycYPI07Se2Te/fCvjX/xotegdD8McClP7Su1+OG7Hxg4eGBeP6JMxMfH8+Pq7zn411+U9vfnmd7aDzX3Fo0aNeLAgQN8/PHHHDp0iEaNGjFy5MgCW4tYo3nQ0Q3QLPj6+uLr65t7xmy4dDECHzuvTGl2BlcuXrtRENKKFKPRSEq8wmAwzfd0cHTEwd2BlKhkzsScZfe1PzlivZ83Z71BSkoKQ/v1p2oSdC4TQMSZcCY//yIvvjGJZi1aADDurcmM7P8CD0dfopyDO4dvXCbK247RvXreUYOVlRWvv/8274weR3OPANxtHfjrWij+jerQsGHDO+6XHREREfjaud82TynA2YejJ8/m8dPJTFxcHMP696KpO/QN9CHsymEmvNCXUe/M4P8aNABM8+qqVauWr3oyooecaYqC/PlhOGXsvDOl2RlcibhH/TAtXmHrYPJDR0dH7NwcSY5O4sSVc2y/uJc9SceY+MlbpKSkMKx/f2qkpdK1fDkiLl7gzcGDeeF//0v3w9emvMnogYNoFBlBgIsL+69eJdLNmU979bqjhlt+OJ5m7gG4Gxz4KyqUso1r35UfelmXus0P/e3KEHIi/344tE8fGtobeMbfn/NnTzFuwHO8MnWa9kPNPUVwcDCzZ88ubhkazX2JboAWIJWrBhG6Nww3x3LpaXFJkfhXLF2Mqu4Oa2trnDxsSYi+gYONC4LgV8aPG3bnCaxUhtq9qvF6p1fw9PRk9apVVEmEHjXqAlDO3ZPK3j5MeW8qjzZrhpWVFf7+/ixd9z0/b9rMxdAwOj38DE2bNk2fB3Qn6tWvzxdrV7Hpp5+IvhrF8BZDqFmzpkUBLzLi7+/P+aRrpBmNWGeo89j1MGrUb5X3DygDq1Ysp7mH8MzDlQAoX8qNyj6evPn+W3y1el2eteaGQmEUvaCApmRTpWpFzt9HfmjvYUtc7A2cbFzA7IeRhvOUCSpPhV71GNZpcrofVk9LpVfthwAo7+FBldLeTJz6fiY//OrHH/h50yYuhYXRpm7dPPjhd+l+OKLFi3fth+HGSNJUGtZyKwDQ6aQzNGrQNIc9c+e7Fctp6mBHlxqmBmY5D3cqeXkx7d13WPLjGu2HGo1Go7l31gEVkbYickJETolI3sYbFRGvjhtOlPVWouLOopQiJv48EWkbeP1/eQ/WUBIYM2k4e5KXcS3xIkZlJDT2KJe9djNv8Sz69O+TPnRqz/adNC6TOUKli509pawMhIeHp6c5ODjQ5aknGTryJR599NFcL7Zu4uHhQc/evRn68ghq1ap1VxcwDg4OPNm/BwtPbuRKfAypxlR+v3CIfw2RtO/YIc/lZWTf79tpVinz0gveLk5YJ8URFxeXr7LvRBrK4kd+EJG3ReSQiBwQkZ9FJNs1Ju50fIqIp4hsEZGT5v8eGV573Zz/hIiUrIgzJZx7wQ9fGTeMq9Y/cy3dD8O4nLb+nvXDlycO56eEZUSY/fDkjaMc89zN7MWz6N2/b7of7t2xg6blMg/Tc7G3x8vaJhs/fIohL79cLH74zMBnWHn5e6KSokg1prI38m9C3ENp36l9nsvLyN4dO2kaUC5TmpezM1bx8doPtR8WKJZ+HxqNpuRxTzRARcQamA20A6oDPUWkevGqup3KlSuzbNUsqjx6hWsOywloEMpX335I7dq1i1vaXfH4Ey2YvmQi8bX3sddpAb4dolnywzz8/Pwy5fPy8yU8NnNocqUUUUkJBb5QfX4YMHgQ3d8YynfqAB9d2oh6ojzzVyzG0dExX+WWKu3LpejMF1ZGoyIuOQ17e/t8lZ0dRbzw+nSl1ENKqTrAeuC2BdByOT7HA78opSoBv5i3Mb/+LFADaAvMMZejyYV7yQ+/WTWLSo9GcMVhGeUahLL42xn3tB++tXgiZ2rsY43dAqzaRLNw9e1+WMrPl8vXMw8zVkpxLTGxRPnhwCED6T9tAL+472BJ6jJcu3qw6Lsv8u2HXr4+XL6R+f0bjUZiU1O1H2o/LGhy/T40Gk3J5F4ZgtsAOKWUOgMgIiuALsDRYlWVDRUrVuSzOdOLW0aBUa9ePRZ8XS/HPF179WT8c4Oo4uWDm4MjSik2nT5GtYYNcHJyKiKlltGydStats7fkNusdOs3gA9fGca73u4429uilGL53ydp3LItNjaFc4gZpWjmPCmlMt5ZcIJsuxByOj67AM3N+b4CtgPjzOkrlFJJwFkROWUu56+Cfxf3HdoPi4l69epRb2kuftizFxMGDqSqT2ncHRxQSrHhxH9Ua1Dy/LBV61a0KmA/7N7/OT54+SUme3riZGdnWs7r36M0bd1a+6H2wwLFwu9Do9GUQESpkn+8isgzQFul1PPm7b5AQ6XUiCz5BgODzZs1gSNFKjR7vIArxS3CTEnRUlJ0QMnSUkUp5ZJbJhHZhEm3pdgDGdeGmK+Umm/pziLyLtAPiAFaKKUis7x+x+NTRKKVUu4Z8kYppTxEZBawSyn1tTl9EbBRKbUqD+/rgUT7YYFRUrSUFB1QsrRoP9R+mCu5fR8Z8pVEP7xJSTruQOvJDa0nZyzy7nulBzS7SS63tZzNJ5H5ACKyTylVv7CF5UZJ0QElR0tJ0QElT4sl+ZRSd1675u7q3QpkF+p0olJqjVJqIjBRRF4HRgBvZC0iO5m5VXsX+2hMaD8sAEqKlpKiA0qeFkvyaT+8vymA7wMomX54E60nZ7SenCmJeizJd680QM8DGaMalAUuFpMWjea+QinV0sKsy4AN3H6Cz+n4DBcRP6XUJRHxAyIs2EeTM/qz02gKCe2HJYsC+D40Gk0J5J4IQgTsBSqJSAURscU0WX9tMWvSaO57RKRShs3OwPFssuV0fK4F+puf9wfWZEh/VkTsRKQCUAnYU9D671O0H2o0xYD2w5KFhd+HRqMpgdwTPaBKqVQRGQFsBqyBL5RS/+aym8VzOgqZkqIDSo6WkqIDtJbcmCoiVTAFkDwHDAEwh7tfqJRqn8vxORVYKSKDgFCgG4BS6l8RWYkpMEcqMFwplVaUb+xeRfthgVFStJQUHaC15Ib2w5JFtt+HBZS035bWkzNaT87ck3ruiSBEGo1Go9FoNBqNRqO597lXhuBqNBqNRqPRaDQajeYeRzdANRqNRqPRaDQajUZTJNx3DVARaSsiJ0TklIiML+S6yonINhE5JiL/ishIc7qniGwRkZPm/x4Z9nndrO2EiLQpBE3WIrJfRNYXpxYRcReRVSJy3Pz5NC4OLSIy2vzdHBGR5SJiX1Q6ROQLEYkQkSMZ0vJct4jUE5HD5tc+FZHsQvZrNLeh/VD7YRYd2g81Dzwi8raIHBKRAyLys5jm8BannulmbzgkIj+IiHsx6+lm9gmjiBTb8h5Fef6yUM9tHlaMWrI93xajHnsR2SMiB816puS6k1LqvnlgmvB/GggCbIGDQPVCrM8PeNj83AX4D6gOfACMN6ePB6aZn1c3a7IDKpi1WhewpjGYwpGvN28XixbgK+B583NbwL2otQD+wFnAwby9EniuqHQAjwEPA0cypOW5bkzREBtjWituI9CusH7T+nH/PLQfaj/MokH7oX7oh1IArhmevwzMK2Y9rQEb8/NpN4+DYtRTDagCbAfqF5OGIj1/WajpNg8rRi3Znm+LUY8AzubnBmA30Cinfe63HtAGwCml1BmlVDKwAuhSWJUppS4ppf4xP78BHMN0ku+C6YID8/8nzc+7ACuUUklKqbPAKbPmAkFEygIdgIUZkotci4i4YjpQFwEopZKVUtHFoQVTpGcHEbEBHDGtrVYkOpRSO4FrWZLzVLeY1opzVUr9pUxH9pIM+2g0OaH9UPthVrQfah54lFLXM2w6AcUajVMp9bNSKtW8uQvTOrDFqeeYUupEcWqgiM9flnAHDysWcjjfFpcepZSKNW8azI8cj6v7rQHqD4Rl2D5PEX0hIhII1MXU6vdRSl0C048EKF1E+j4BxmIKSX6T4tASBEQCX5qHvy0UEaei1qKUugDMwBTu/hIQo5T6uah1ZCGvdfubnxemJs39ifZD7YfpaD/UaG4hIu+KSBjQG5hc3HoyMBBTz/6DTrGdv+41spxvi1OHtYgcACKALUqpHPXcbw3Q7OaCFPqdLRFxBr4HRmW5s3Zb1mzSCkSfiHQEIpRSf1u6S2FpwXSX/WFgrlKqLhCHaXhVkWoxzyfqgmkIVxnASUT6FLUOC7lT3cWpSXNvo/1Q++GtQrUfah4gRGSrea5z1kcXAKXURKVUOeAbYERx6zHnmYhpHdhvSoKeYkYf6xaQh/NtoaOUSlNK1cHUg99ARGrmlN+mSFQVHeeBchm2y2IaYlRoiIgB05f/jVJqtTk5XET8lFKXzEOGIopAX1Ogs4i0B+wBVxH5upi0nAfOZ7j7sQrTBVdRa2kJnFVKRQKIyGqgSTHoyEhe6z5P5uE4hf6b1tw3aD/UfpgR7YeaBwalVEsLsy4DNgBvFKKcXPWISH+gI/CEeXh5oZKHz6e4KPLz173GHc63xY5SKlpEtgNtgTsGbLrfekD3ApVEpIKI2ALPAmsLqzJz9L1FwDGl1EcZXloL9Dc/7w+syZD+rIjYiUgFoBKmgAr5Rin1ulKqrFIqENP7/lUp1aeYtFwGwkSkijnpCeBoMWgJBRqJiKP5u3oC0zj5Iv9MMpCnus3D0m6ISCPze+iXYR+NJie0H2o/zIj2Q40GEJFKGTY7A8eLSwuYor0C44DOSqn44tRSgijS89e9Rg7n2+LS4y3m6M0i4oDphmfOx5Uq5khOBf0A2mOKBnUamFjIdT2CaUjAIeCA+dEeKAX8Apw0//fMsM9Es7YTFFL0PqA5t6I+FosWoA6wz/zZ/Ah4FIcWYIr5IDgCLMUUVbFIdADLMc21SsF0N2/Q3dQN1DfrPw3MAqQwf9f6cf88tB9qP8yiQ/uhfjzwD0y9RkfMx+M6wL+Y9ZzCNN/xpm8Wd1Tep8zHaBIQDmwuJh1Fdv6yUM9tHlaMWrI93xajnoeA/WY9R4DJue0j5h01Go1Go9FoNBqNRqMpVO63IbgajUaj0Wg0Go1Goymh6AaoRqPRaDQajUaj0WiKBN0A1Wg0Go1Go9FoNBpNkaAboBqNRqPRaDQajUajKRJ0A1Sj0Wg0Go1Go9FoNEWCboA+oIhIORE5KyKe5m0P83ZANnkdRGSHiFjnofwRIjKgIDVrNBpNYaD9UKPRPOiIiBKRDzNsvyoibxajJERksdmLD4jIPyLSuBDr+klE3M2PYYVVj8aEboA+oCilwoC5wFRz0lRgvlLqXDbZBwKrlVJpeajiC+Dl/KnUaDSawkf7oUaj0ZAEPC0iXoVdUV5u4AGvKaXqAOOBzy0sX0QkT20cpVR7pVQ04A7oBmghoxugDzYfA41EZBSmRW0/vEO+3sAaABFpbr77v1JE/hORqSLSW0T2iMhhEakIoJSKB0JEpEERvA+NRqPJL9oPNRrNg0wqMB8YnfUFEekmIkdE5KCI7DSnWYvIDLPXHRKRl8zpT4jIfnP6FyJiZ04PEZHJIvI70E1EWovIX+aeze9ExDkXfTuBYHNZY8x6jpg9GxEJFJFjIjIH+AcoJyLTzXkOi0gPcz4/Edlp7lU9IiKPZtDnhekGZEXz69NFZKmIdMnwWXwjIp3v/mPWgG6APtAopVKA1zBdeI1SSiVnzSMitkCQUiokQ3JtYCRQC+gLVFZKNQAWAi9lyLcPeLRw1Gs0Gk3Bof1Qo9FomA30FhG3LOmTgTZKqdrAzcbXYKACUFcp9RDwjYjYA4uBHkqpWoANMDRDOYlKqUeArcAkoKVS6mFM/jgmF22dgMMiUg8YADQEGgEviEhdc54qwBKlVF2gPlAHk0e3BKaLiB/QC9hs7lWtDRzIUs944LRSqo5S6jVMXj4AwPy5NAF+ykWrJhd0A1TTDrgE1LzD615AdJa0vUqpS0qpJOA08LM5/TAQmCFfBFCmwJRqNBpN4aL9UKPRPLAopa4DS7h9ysAfwGIReQG4OXy2JTBPKZVq3vcapgbgWaXUf+Y8XwGPZSjnW/P/RkB14A8ROQD0B26bc29mujnPYGAQphEqPyil4pRSscBqbt3cO6eU2mV+/giwXCmVppQKB3YA/wfsBQaY57fWUkrdyOUz2QEEi0hpoCfw/c33rLl7dAP0AUZE6gCtMBnBaPOdoawkAPZZ0pIyPDdm2DZiutt1E3vz/hqNRlOi0X6o0Wg0AHyCqaHndDNBKTUEU49lOeCAiJQCBFBZ9pVcyo7LkG+LuZexjlKqulJq0B32ec2cp5VS6kgudcRleJ5tPqXUTkyN4gvAUhHpl4tmgKWYpl8MAL60IL8mF3QD9AFFRART0I1RSqlQYDowI2s+pVQUYG0eVpFXKgNH8iVUo9FoChnthxqNRmPC3JO5ElMjFAARqaiU2q2UmgxcwdQQ/RkYIiI25jyewHEgUESCzbv2xdTzmJVdQNOb+UTEUUQqWyhxJ/CkeR8n4Cngtzvk62Geq+qNqdG5R0zRzSOUUguARcDDWfa7AbhkSVsMjAJQSv1roU5NDugG6IPLC0CoUmqLeXsOUFVEmmWT92dMQxnySlNM4/w1Go2mJKP9UKPRaG7xIaYpBzeZbg7kcwRTw+4gprmRocAhETkI9FJKJWLqJfxORA5jGgkyL2vhSqlI4DlguYgcwtQgrWqJMKXUP5gahHuA3cBCpdT+bLL+ABwya/0VGKuUugw0x9SLux/oCszMUv5VTEODj4jIdHNaOHAM3ftZYIhSWXvPNZrMmCd3j1FK9S3MfTQajaako/1Qo9FoHixExBHTvP6HlVIxxa3nfkD3gGpyxXxnaZvkbd0mL+B/hSRJo9FoigXthxqNRvPgICItMQ0t/kw3PgsO3QOq0Wg0Go1Go9FoNJoiQfeAajQajUaj0Wg0Go2mSNANUI1Go9FoNBqNRqPRFAm6AarRaDQajUaj0Wg0miJBN0A1Go1Go9FoNBqNRlMk6AaoRqPRaDQajUaj0WiKhP8H5LBuP61wFE4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                                    # set the color map\n",
    "plt.subplot(131)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(132)                                        # location map of normal score transform of permeability\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(133)\n",
    "facies = df['Facies'].values +0.01                      # normal score porosity / permeability scatter plot color coded by facies\n",
    "plt.scatter(df['NPor'],df['NPerm'],c = facies,edgecolor = 'black',cmap = plt.cm.inferno)\n",
    "#plt.plot([-3,3],[-3,3],color = 'black')\n",
    "plt.xlabel(r'Nscore Porosity')\n",
    "plt.ylabel(r'Nscore Permeability')\n",
    "plt.title('Nscore Permeability vs. Porosity')\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=0.8, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experimental Variograms\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters. For example:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n",
    "\n",
    "#### Dashboard for Interactive Variogram Calculation\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating experimental variograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmath; import math\n",
    "\n",
    "# interactive calculation of the experimental variogram\n",
    "l = widgets.Text(value='                              Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "lag = widgets.FloatSlider(min = 20, max = 500, value = 100, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "lag.style.handle_color = 'gray'\n",
    "\n",
    "lag_tol = widgets.FloatSlider(min = 1, max = 500, value = 50, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "lag_tol.style.handle_color = 'gray'\n",
    "\n",
    "nlag = widgets.IntSlider(min = 1, max = 100, value = 10, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "nlag.style.handle_color = 'gray'\n",
    "\n",
    "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "azi.style.handle_color = 'gray'\n",
    "\n",
    "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 20, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'),continuous_update=False)\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'),continuous_update=False)\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "\n",
    "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting    \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth):     # function to take parameters, calculate variogram and plot\n",
    "#    text_trap = io.StringIO()\n",
    "#    sys.stdout = text_trap\n",
    "    tmin = -9999.9; tmax = 9999.9\n",
    "    lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=1.0)\n",
    "    plt.subplot(121)                                        # location map of normal score transform of porosity\n",
    "    GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "#     pt = cmath.rect(200, math.radians(azi-90))  \n",
    "#     x = pt.real+500  \n",
    "#     y = pt.imag+500\n",
    "    \n",
    "#     plt.plot([500,x],[500,y],color='red')\n",
    "    \n",
    "    plt.subplot(122)                                    # plot experimental variogram\n",
    "    scatter = plt.scatter(lags,gammas,color = 'darkorange',edgecolor='black',s = npps*0.05,label = 'Azimuth ' +str(azi))\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol < 90.0:\n",
    "        plt.title('Directional NSCORE Porosity Variogram - Azi ' + str(azi))\n",
    "    else:\n",
    "        plt.title('Omnidirectional NSCORE Porosity Variogram - Azi ' + str(azi))\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.annotate(r'Sill = $\\sigma^2$',[905,1.03])\n",
    "    plt.grid(True)\n",
    "    \n",
    "    legend = plt.legend(*scatter.legend_elements(\"sizes\", num=6),loc='upper left')\n",
    "    legend.set_title('Number of Pairs/20')\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.5, top=1.1, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Variogram Calculation Demostration\n",
    "\n",
    "* calculate omnidirectional and direction experimental variograms \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data.\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance\n",
    "\n",
    "* **number of lags** - number of lags in the experimental variogram\n",
    "\n",
    "* **bandwidth** - maximum departure from the lag vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a63d38bdde314d64bd8af0eb92d1c6dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Calculation Interactive Demonstration, Mich…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49c2eb43db644d5bbaaed2b8f735b8d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of vairogram calculation for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
